### Journal Papers (Refereed)

- M. Romero, F. K. Nakano, J. Finke, C. Rocha, and C. Vens, “Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification,” Submitted for publication (Computers in Biology and Medicine), 2022.

[Bibtex]`@article{J006, author={Miguel Romero and Felipe Kenji Nakano and Jorge Finke and Camilo Rocha and Celine Vens}, title={Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification}, journal={Submitted for publication (Computers in Biology and Medicine)}, volume={}, number={}, pages={}, year={2022}, abstract = {}, doi={}, }`

- A. Jaramillo-Botero, J. Colorado, M. A. et al. Quimbaya, M. C. Rebolledo, M. Lorieux, T. Ghneim-Herrera, C. Arango, L. E. Tobon, J. Finke, C. Rocha, F. Munoz, J. J. Riascos, F. Silva, N. Chirinda, M. Caccamo, K. Vandepoele, and W. A. Goddard, “The OMICAS Alliance, an International Research Program on Multi-Omics for Crop,” Submitted for publication (Frontiers in Plant Science), 2022.

[Bibtex]`@article{J005, author={Andres Jaramillo-Botero and Julian Colorado and et al. Mauricio Alberto Quimbaya and Maria Camila Rebolledo and Mathias Lorieux and Thaura Ghneim-Herrera and Carlos Arango and Luis E Tobon and Jorge Finke and Camilo Rocha and Fernando Munoz and John J. Riascos and Fernando Silva and Ngonidzashe Chirinda and Mario Caccamo and Klaas Vandepoele and William A Goddard}, title={The OMICAS Alliance, an International Research Program on Multi-Omics for Crop}, journal={Submitted for publication (Frontiers in Plant Science)}, volume={}, number={}, pages={}, year={2022}, abstract = {}, doi={}, }`

- M. Peñuela, C. Riccio-Rengifo, J. Finke, C. Rocha, A. Gkanogiannis, R. A. Wing, and M. Lorieux, “Sequence identity allows prediction of crossover recombination,” Submitted for publication (PlosOne), 2022.

[Bibtex]`@article{J004, author={Mauricio Pe\~nuela and Camila Riccio-Rengifo and Jorge Finke and Camilo Rocha and Anestis Gkanogiannis and Rod A. Wing and Mathias Lorieux}, title={Sequence identity allows prediction of crossover recombination}, journal={Submitted for publication (PlosOne)}, volume={}, number={}, pages={}, year={2022}, abstract = {}, doi={}, }`

- J. Medina and J. Finke, “Characterizing the head of the degrees distributions of growing networks,” Submitted for publication, 2021.

[Bibtex]`@article{J003, author={Jan Medina and Jorge Finke}, title={Characterizing the head of the degrees distributions of growing networks}, journal={Submitted for publication}, volume={}, number={}, pages={}, year={2021}, abstract = {}, doi={}, }`

- J. Romero and J. Finke, “Degree distribution in dynamic networks with random growth,” submitted for publication (Mathematical Models and Methods in Applied Sciences), 2021.

[Bibtex]`@article{J002, author={Juan Romero and Jorge Finke}, title={Degree distribution in dynamic networks with random growth}, journal={submitted for publication (Mathematical Models and Methods in Applied Sciences)}, volume={}, number={}, pages={}, year={2021}, abstract = {Several network models consider the outcome of preferential attachment in scenarios in which new nodes establish a constant number of edges. Such modeling efforts explain degree distributions that follow a pure power-law. They tend to focus on capturing the tail of the degree distribution, that is, the behavior of the nodes that greatly exceed the average (the so-called hubs). Less attention has been paid to developing models that resemble the head of the distribution of empirical networks. This paper explores the combined effect of the attachment mechanism and the number of edges established by the nodes that join a network. The proposed model generates a heavy-tailed distributions that capture various forms of behavior for nodes with relatively small degree (the head of the distribution). We fit the model to various empirical networks whose head of the distribution follows a power-law, an exponential, and Poisson distribution.}, doi={}, }`

- K. Guerrero and J. Finke, “Stationary Distributions of Networks with Homophilic and Heterophilic Interactions,” submitted for publciation (IEEE Transactions on Network Science and Engineering), 2021.

[Bibtex]`@article{J001, author={Katerine Guerrero and Jorge Finke}, title={Stationary Distributions of Networks with Homophilic and Heterophilic Interactions}, journal={submitted for publciation (IEEE Transactions on Network Science and Engineering)}, volume={}, number={}, pages={}, year={2021}, abstract = {A handful of network models have been proposed to rigorously explain the tendency of nodes to connect to other nodes with shared attributes. This work provides new insights into the dynamics of interactions resulting from preferential linkage mechanisms based on node types. It introduces a model that obeys two simple linkage mechanisms for any group of same-type nodes. The first mechanism encourages homophilic interactions by disconnecting from a neighbor of different type and connecting to a new node that is selected uniformly at random. The second mechanism, in contrast, encourages heterophilic interactions by disconnecting from a random neighbor and connecting to a node of different type. Our main result is the characterization of the stationary probability distribution that a group of nodes exhibits a certain amount of homophily. Bounds on the expected homophily of a group illustrate the effects of group preference and group size. We also present simulation results which show how the mechanisms impact homophily as an aggregate outcome at the network level. Moreover, we propose an approach for link prediction based on homophily at the node and group level.}, doi={}, }`

- M. Romero, Ó. Ramírez, J. Finke, and C. Rocha, “Feature extraction with spectral clustering for gene function prediction using hierarchical multi-label classification,” Applied Network Science, vol. 7, iss. 28, p. 1–20, 2022.

[Bibtex]`@article{s41109-022-00468-w, author={Miguel Romero and \'Oscar Ram\'irez and Jorge Finke and Camilo Rocha}, title={Feature extraction with spectral clustering for gene function prediction using hierarchical multi-label classification}, journal={Applied Network Science}, volume={7}, number={28}, pages={1--20}, year={2022}, abstract={Gene annotation addresses the problem of predicting unknown associations between gene and functions (e.g., biological processes) of a specifc organism. Despite recent advances, the cost and time demanded by annotation procedures that rely largely on in vivo biological experiments remain prohibitively high. This paper presents a novel in silico approach for to the annotation problem that combines cluster analysis and hierarchical multi-label classifcation (HMC). The approach uses spectral clustering to extract new features from the gene co-expression network (GCN) and enrich the prediction task. HMC is used to build multiple estimators that consider the hierarchical structure of gene functions. The proposed approach is applied to a case study on Zea mays, one of the most dominant and productive crops in the world. The results illustrate how in silico approaches are key to reduce the time and costs of gene annotation. More specifcally, they highlight the importance of: (1) building new features that represent the structure of gene relationships in GCNs to annotate genes; and (2) taking into account the structure of biological processes to obtain consistent predictions.}, doi={10.1007/s41109-022-00468-w}, }`

- M. Romero, J. Finke, and C. Rocha, “A top‑down supervised learning approach to hierarchical multi‑label classification in networks,” Applied Network Science, vol. 7, iss. 8, p. 1–17, 2022.

[Bibtex]`@article{s41109‑022‑00445‑3, author={Miguel Romero and Jorge Finke and Camilo Rocha}, title={A top‑down supervised learning approach to hierarchical multi‑label classification in networks}, journal={Applied Network Science}, volume={7}, number={8}, pages={1--17}, year={2022}, abstract = {Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. This paper presents a general prediction model to hierarchical multi-label classification, where the attributes to be inferred can be specified as a strict poset. It is based on a top-down classification approach that addresses hierarchical multi-label classification with supervised learning by building a local classifier per class. The proposed model is showcased with a case study on the prediction of gene functions for Oryza sativa Japonica, a variety of rice. It is compared to the Hierarchical Binomial-Neighborhood, a probabilistic model, by evaluating both approaches in terms of prediction performance and computational cost. The results in this work support the working hypothesis that the proposed model can achieve good levels of prediction efficiency, while scaling up in relation to the state of the art.}, doi={10.1007/s41109‑022‑00445‑3}, }`

- C. Riccio, J. Finke, and C. Rocha, “Identifying stress responsive genes using overlapping communities in co-expression networks,” BMC Bioinformatics, vol. 22, iss. 541, p. 1–17, 2022.

[Bibtex]`@article{s12859-021-04462-4, author={Camila Riccio and Jorge Finke and Camilo Rocha}, title={Identifying stress responsive genes using overlapping communities in co-expression networks}, journal={BMC Bioinformatics}, volume={22}, number={541}, pages={1--17}, year={2022}, abstract = {This paper proposes a workflow to identify genes responding to a specific treatment in an organism, such as abiotic stresses, a main cause of extensive agricultural production losses worldwide. On input RNA sequencing read counts (measured for genotypes under control and treatment conditions) and biological replicates, it outputs a collection of characterized genes, potentially relevant to treatment. Technically, the proposed approach is both a generalization and an extension of WGCNA; its main goal is to identify specific modules in a network of genes after a sequence of normalization and filtering steps. In this work, module detection is achieved by using Hierarchical Link Clustering, which can recognize overlapping communities and thus have more biological meaning given the overlapping regulatory domains of systems that generate co-expression. Additional steps and information are also added to the workflow, where some networks in the intermediate steps are forced to be scale-free and LASSO regression is employed to select the most significant modules of phenotypical responses to stress. Finally, the workflow is showcased with a systematic study on rice (Oryza sativa), a major food source that is known to be highly sensitive to salt stress: a total of 6 modules are detected as relevant in the response to salt stress in rice; these genes may act as potential targets for the improvement of salinity tolerance in rice cultivars. The proposed workflow has the potential to ultimately reduce the search-space for candidate genes responding to a specific treatment, which can considerably optimize the effort, time, and money invested by researchers in the experimental validation of stress responsive genes.}, doi={10.1186/s12859-021-04462-4}, }`

- J. Campos and J. Finke, “Measuring event concentration in empirical networks with different types of degree distributions,” PLoS ONE, vol. 15, iss. 12, p. e0241790, 2020.

[Bibtex]`@article{0241790, author={Juan Campos and Jorge Finke}, title={Measuring event concentration in empirical networks with different types of degree distributions}, journal={{PLoS ONE}}, volume={15}, number={12}, pages={e0241790}, year={2020}, abstract = {Measuring event concentration often involves identifying clusters of events at various scales of resolution and across different regions. In the context of a city, forexample, clusters may be characterized by the proximity of events in the metric space. However, events may also occur over urban structures such as public transportation and infrastructure systems, which are naturally represented as networks. Our work provides a theoretical framework to determine whether events distributed over a set of interconnected nodes are concentrated on a particular subset. Our main analysis shows how the proposed or any other measure of event concentration on a network must explicitly take into account its degree distribution. We apply the framework to measure event concentration (i) on a street network (i.e., approximated as a regular network where events represent criminal activities); and (ii) on a social network (i.e., a power law network where events represent users who are dissatisfied after purchasing the same product).}, doi={10.1371/journal.pone.0241790}, }`

- D. Ruiz, J. Campos, and J. Finke, “Dynamics in affinity-weighted preferential attachment networks,” Journal of Statistical Physics, vol. 181, iss. 2, p. 673–689, 2020.

[Bibtex]`@article{s10955-020-02594-0, author={Diego Ruiz and Juan Campos and Jorge Finke}, title={Dynamics in affinity-weighted preferential attachment networks}, journal={Journal of Statistical Physics}, volume={181}, number={2}, pages={673--689}, year={2020}, abstract = {During the formation process of stochastic networks, nodes tend to establish edges based on selective linkage mechanisms. In general, these mechanism involve probability distributions that underlie the selection of target nodes. In social networks, edges are often associated to relationships that are homophilic with respect to individual traits. Such traits include, for example, gender and age, and are referred to as node types. Our work considers an affinity-weighted preferential attachment model that characterizes the tendency of two group of nodes to connect to other nodes of the same type. We derive mathematical expressions for the dynamics and convergence of homophily measures at the node, group, and network level. Furthermore, we characterize the convergence of network modularity and show that the formation of community structures can be expressed as a function of network homophily.}, doi={s10955-020-02594-0}, }`

- M. Romero, J. Finke, C. Rocha, and L. Tobón, “Spectral evolution with approximated eigenvalue trajectories for link prediction,” Social Network Analysis and Mining, vol. 10, iss. 60, p. 1–10, 2020.

[Bibtex]`@article{s13278-020-00674-3, author={Miguel Romero and Jorge Finke and Camilo Rocha and Luis Tob{\'o}n}, title={Spectral evolution with approximated eigenvalue trajectories for link prediction}, journal={Social Network Analysis and Mining}, volume={10}, number={60}, pages={1--10}, year={2020}, abstract = {The spectral evolution model aims to characterize the growth of large networks (i.e., how they evolve as new edges are established) in terms of the eigenvalue decomposition of the adjacency matrices. It assumes that, while eigenvectors remain constant, eigenvalues evolve in a predictable manner over time. This paper extends the original formulation of the model twofold. First, it presents a method to compute an approximation of the spectral evolution of eigenvalues based on the Rayleigh quotient. Second, it proposes an algorithm to estimate the evolution of eigenvalues by extrapolating only a fraction of their approximated values. The proposed model is used to characterize mention networks of users who posted tweets that include the most popular political hashtags in Colombia from August 2017 to August 2018 (the period which concludes the disarmament of the Revolutionary Armed Forces of Colombia). To evaluate the extent to which the spectral evolution model resembles these networks, link prediction methods based on learning algorithms (i.e., extrapolation and regression) and graph kernels are implemented. Experimental results show that the learning algorithms deployed on the approximated trajectories outperform the usual kernel and extrapolation methods at predicting the formation of new edges.}, doi={s13278-020-00674-3}, }`

- K. Guerrero and J. Finke, “A Markov chain analysis of the dynamics of homophily,” Journal of Complex Networks, vol. 8, iss. 1, 2020.

[Bibtex]`@article{cnz022, author={Katerine Guerrero and Jorge Finke}, title={A Markov chain analysis of the dynamics of homophily}, journal={Journal of Complex Networks}, volume={8}, number={1}, month = {06}, year={2020}, abstract = {Many networks consist of different types of nodes, which are characterized based on similar quantitative or qualitative properties. Understanding the effects of homophilic relationships, i.e., the tendency of nodes to establish links to other nodes that are alike, requires of formal frameworks that explain how local decision-making mechanisms contribute to the formation of particular network structures. Based on two simple stochastic mechanisms that encourage and discourage establishing links to similar others, this paper introduces a model that explains the emergence of homophily as an aggregate group- and network-level outcome. We characterize the dynamics of homophily and present conditions that guarantee that the expected value exceeds that of a purely random decision-making process. Moreover, we show that the proposed model resembles patterns of homophily in a citation network of political blogs. Finally, we use the model to design a personalized system that offers different recommendations based on the degree of preference for other nodes of the same type.}, doi={10.1093/comnet/cnz022}, url={https://academic.oup.com/comnet/advance-article/doi/10.1093/comnet/cnz022/5525361?guestAccessKey=03f6a1f7-e284-488f-b713-4289b15c6eb1} }`

- I. Fernandez, K. Passino, and J. Finke, “Stability of degree distributions of social networks,” Journal of Complex Networks, vol. 7, iss. 3, p. 421–444, 2019.

[Bibtex]`@article{cny026, author={Isabel Fernandez and Kevin Passino and Jorge Finke}, title={Stability of degree distributions of social networks}, journal={Journal of Complex Networks}, volume={7}, number={3}, pages={421--444}, year={2019}, abstract = {Social network models formalize mechanisms of connection and provide a framework to study emerging topological properties in relationships, interactions, and communications between social actors. Random and preferential attachment are widely-used mechanisms which assume that newly added actors establish a fixed number of connections. Our work extends the class of random and preferential attachment models by considering scenarios in which the number of new connections may vary over time. For the original mechanisms, we show that infinite-dimensional time-varying linear systems characterize the evolution of the cumulative degree distributions. Moreover, we show that the limit average degree and the limit degree distribution are stable invariants. The stability of these sets implies that small perturbations to the network can only lead to small variations in the two topological measures at any point in time. We present analogous results for preferential attachment models where the number of new connections is defined by random variables obeying a binomial or power law probability function. Finally, simulations illustrate how random and targeted perturbations impact the invariants.}, doi={10.1093/comnet/cny026}, }`

- D. Ruiz and J. Finke, “Lyapunov-based anomaly detection in preferential attachment networks,” International Journal of Applied Mathematics and Computer Science, vol. 29, iss. 2, p. 363–373, 2019.

[Bibtex]`@article{amcs-2019-0027, author={Diego Ruiz and Jorge Finke}, title={Lyapunov-based anomaly detection in preferential attachment networks}, journal={International Journal of Applied Mathematics and Computer Science}, volume={29}, number={2}, pages={363--373}, year={2019}, abstract = {Network models aim to explain patterns of empirical relationships based on mechanisms that operate under various principles for establishing and removing links. The principle of preferential attachment forms a basis for the well-known Barabási–Albert model, which describes a stochastic preferential attachment process where newly added nodes tend to connect to the more highly connected ones. Previous work has shown that a wide class of such models are able to recreate power law degree distributions. This paper characterizes the cumulative degree distribution of the Barabási–Albert model as an invariant set and shows that this set is not only a global attractor, but it is also stable in the sense of Lyapunov. Stability in this context means that, for all initial configurations, the cumulative degree distributions of subsequent networks remain, for all time, close to the limit distribution. We use the stability properties of the distribution to design a semi-supervised technique for the problem of anomalous event detection on networks.}, doi={10.2478/amcs-2019-0027}, }`

- P. Moriano, J. Finke, and Y. -Y. Ahn, “Community-based event detection in temporal networks,” Scientific Reports, vol. 9, iss. 1, p. 1–9, 2019.

[Bibtex]`@article{s41598-019-40137-0, author={Pablo Moriano and Jorge Finke and Y.-Y. Ahn}, title={Community-based event detection in temporal networks}, journal={Scientific Reports}, volume={9}, number={1}, pages={1--9}, year={2019}, abstract = {We propose a method for detecting large events based on the structure of temporal communication networks. Our method is motivated by findings that viral information spreading has distinctive diffusion patterns with respect to community structure. Namely, we hypothesize that global events trigger viral information cascades that easily cross community boundaries and thus can be detected by monitoring intra- and inter-community communications. By comparing the amount of communication within and across communities, we show that it is possible to detect events, even when they do not trigger a significantly larger communication volume. We demonstrate the effectiveness of our method using two examples?the email communication network of Enron and the Twitter communication network during the Boston Marathon bombing.}, doi={10.1038/s41598-019-40137-0}, }`

- J. Medina, J. Finke, and C. Rocha, “Estimating formation mechanisms and degree distributions in mixed attachment networks,” Journal of Physics A: Mathematical and Theoretical, vol. 52, iss. 9, p. 95001, 2019.

[Bibtex]`@article{1751-8121/aaffeb, author={Jan Medina and Jorge Finke and Camilo Rocha}, title={Estimating formation mechanisms and degree distributions in mixed attachment networks}, journal={Journal of Physics A: Mathematical and Theoretical}, volume={52}, number={9}, pages={095001}, year={2019}, abstract = {Our work introduces an approach for estimating the contribution of attachment mechanisms to the formation of growing networks. We present a generic model in which growth is driven by the continuous attachment of new nodes according to random and preferential linkage with a fixed probability. Past approaches apply likelihood analysis to estimate the probability of occurrence of each mechanism at a particular network instance, exploiting the concavity of the likelihood function at each point in time. However, the probability of connecting to existing nodes, and consequently the likelihood function itself, varies as networks grow. We establish conditions under which applying likelihood analysis guarantees the existence of a local maximum of the time-varying likelihood function and prove that an expectation maximization algorithm provides a convergent estimate. Furthermore, the in-degree distributions of the nodes in the growing networks are analytically characterized. Simulations show that, under the proposed conditions, expectation maximization and maximum-likelihood accurately estimate the actual contribution of each mechanism, and in-degree distributions converge to stationary distributions.}, doi={10.1088/1751-8121/aaffeb}, url={https://iopscience.iop.org/article/10.1088/1751-8121/aaffeb/meta}, }`

- D. Ruiz and J. Finke, “Lyapunov-based anomaly detection in highly-clustered networks,” Journal of Statistical Physics, vol. 172, iss. 4, pp. 1127-1146, 2018.

[Bibtex]`@article{s10955-018-2089-7, author={Diego Ruiz and Jorge Finke}, title={Lyapunov-based anomaly detection in highly-clustered networks}, journal={Journal of Statistical Physics}, volume={172}, number={4}, pages={1127-1146}, issn = {0022-4715}, year={2018}, abstract = {Network formation models explain the dynamics of the structure of connections using mechanisms that operate under different principles for establishing and removing edges. The Jackson-Rogers model is a generic framework that applies the principle of triadic closure to networks that grow by the addition of new nodes and new edges over time. Past work describes the limit distribution of the in-degree of the nodes based on a continuous-time approximation. Here, we introduce a discrete-time approach of the dynamics of the in- and out-degree distributions of a variation of the model. Furthermore, we characterize the limit distributions and the expected value of the average degree as equilibria, and prove that the equilibria are asymptotically stable. Finally, we use the stability properties of the model to propose a detection criterion for anomalies in the edge formation process.}, doi = {10.1007/s10955-018-2089-7}, url={https://link.springer.com/article/10.1007/s10955-018-2089-7} }`

- P. Moriano and J. Finke, “On the formation of structure in growing networks,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2013, iss. 06, p. P06010, 2013.

[Bibtex]`@article{1742-5468-2013-06-P06010, author={Pablo Moriano and Jorge Finke}, title={On the formation of structure in growing networks}, journal={Journal of Statistical Mechanics: Theory and Experiment}, volume={2013}, number={06}, pages={P06010}, url={http://stacks.iop.org/1742-5468/2013/i=06/a=P06010}, year={2013}, abstract = { Based on the formation of triad junctions, the mechanism proposed in this paper generates networks that exhibit extended rather than single power law behavior. Triad formation guarantees strong neighborhood clustering and community-level characteristics as the network size grows to infinity. The asymptotic behavior is of interest in the study of directed networks in which (i) the formation of links cannot be described according to the principle of preferential attachment, (ii) the in-degree distribution fits a power law for nodes with a high degree and an exponential form otherwise, (iii) clustering properties emerge at multiple scales and depend on both the number of links that newly added nodes establish and the probability of forming triads, and (iv) groups of nodes form modules that feature fewer links to the rest of the nodes.}, doi={10.1088/1742-5468/2013/06/P06010}, }`

- P. Moriano and J. Finke, “Power-law weighted networks from local attachments,” EPL (Europhysics Letters), vol. 99, iss. 1, p. 18002, 2012.

[Bibtex]`@article{0295-5075-99-1-18002, author={P. Moriano and J. Finke}, title={Power-law weighted networks from local attachments}, journal={EPL ({Europhysics Letters})}, volume={99}, number={1}, pages={18002}, url={http://stacks.iop.org/0295-5075/99/i=1/a=18002}, year={2012}, abstract={This letter introduces a mechanism for constructing, through a process of distributed decision-making, substrates for the study of collective dynamics on extended power-law weighted networks with both a desired scaling exponent and a fixed clustering coefficient. The analytical results show that the connectivity distribution converges to the scaling behavior often found in social and engineering systems. To illustrate the approach of the proposed framework we generate network substrates that resemble steady state properties of the empirical citation distributions of i) publications indexed by the Institute for Scientific Information from 1981 to 1997; ii) patents granted by the U.S. Patent and Trademark Office from 1975 to 1999; and iii) opinions written by the Supreme Court and the cases they cite from 1754 to 2002.}, doi={10.1209/0295-5075/99/18002}, }`

- J. Finke and K. M. Passino, “Local agent requirements for stable emergent group distributions,” IEEE Transactions on Automatic Control, vol. 56, iss. 6, pp. 1426-1431, 2011.

[Bibtex]`@article{5710392, author = {J. Finke and K. M. Passino}, journal = {{IEEE} Transactions on Automatic Control}, title = {Local agent requirements for stable emergent group distributions}, year = {2011}, volume = {56}, number = {6}, pages = {1426-1431}, abstract = {This note introduces a model of a generic team formation problem. We derive general conditions under which a group of scattered decision-making agents converge to a particular distribution. The desired distribution is achieved when the agents divide themselves into a fixed number of sub-groups while consenting on gains which are associated to every subgroup. The model allows us to quantify the impact of limited sensing, motion, and communication capabilities on the rate at which the distribution is achieved. Finally, we show how this theory is useful in solving a cooperative surveillance problem.}, doi={10.1109/TAC.2011.2112476}, ISSN={0018-9286}, }`

- B. J. Moore, J. Finke, and K. M. Passino, “Optimal allocation of heterogeneous resources in cooperative control scenarios,” Automatica, vol. 45, iss. 3, pp. 711-715, 2009.

[Bibtex]`@article{2008-09-007, title = "Optimal allocation of heterogeneous resources in cooperative control scenarios ", journal = "Automatica ", volume = "45", number = "3", pages = "711 - 715", year = "2009", issn = "0005-1098", doi = "10.1016/j.automatica.2008.09.007", url = "http://www.sciencedirect.com/science/article/pii/S0005109808004767", author = "Brandon J. Moore and Jorge Finke and Kevin M. Passino", keywords = "Cooperative control", keywords = "Constrained optimization", keywords = "Convex optimization", keywords = "Dynamic optimization ", abstract = "This paper introduces a mathematical framework for the study of resource allocation problems involving the deployment of heterogeneous agents to different teams. In this context, the term heterogeneous is used to describe classes of agents that differ in the basic functions they can perform (e.g., one type of agent searches for targets while another type engages those targets). The problem is addressed in terms of optimization using concepts from economic theory and the proposed algorithm was designed to ensure asymptotic stability of the optimal solution. " }`

- J. Finke, N. Quijano, and K. M. Passino, “Emergence of scale-free networks from Ideal Free Distributions,” EPL (Europhysics Letters), vol. 82, iss. 2, p. 28004, 2008.

[Bibtex]`@article{0295-5075-82-28004, author = {Jorge Finke and Nicanor Quijano and Kevin M. Passino}, title = {Emergence of scale-free networks from {Ideal Free Distributions}}, DOI = {10.1209/0295-5075/82/28004}, url = {http://dx.doi.org/10.1209/0295-5075/82/28004}, journal = {EPL ({Europhysics Letters})}, year = {2008}, volume = {82}, number = {2}, pages = {28004} }`

- J. Finke and K. M. Passino, “Stable cooperative vehicle distributions for surveillance,” Journal of Dynamic Systems, Measurement, and Control, vol. 129, iss. 5, p. 597–608, 2007.

[Bibtex]`@article{10-1115-1-2767656, author = {Finke, Jorge and Passino, Kevin M.}, abstract = {A mathematical model for the study of the behavior of a spatially distributed group of heterogeneous vehicles is introduced. We present a way to untangle the coupling between the assignment of any vehicle's position and the assignment of all other vehicle positions by defining general sensing and moving conditions that guarantee that even when the vehicles'motion and sensing are highly constrained, they ultimately achieve a stable emergent distribution. The achieved distribution is optimal in the sense that the proportion of vehicles allocated over each area matches the relative importance of being assigned to that area. Based on these conditions, we design a cooperative control scheme for a multivehicle surveillance problem and show how the vehicles'maneuvering and sensing abilities, and the spatial characteristics of the region under surveillance, affect the desired distribution and the rate at which it is achieved.}, date = {2007/01/17}, isbn = {0022-0434}, journal = {Journal of Dynamic Systems, Measurement, and Control}, doi = {10.1115/1.2767656}, month = {01}, number = {5}, pages = {597--608}, title = {Stable cooperative vehicle distributions for surveillance}, url = {http://dx.doi.org/10.1115/1.2767656}, volume = {129}, year = {2007}, }`

- J. Finke, K. M. Passino, and A. G. Sparks, “Stable task load balancing strategies for cooperative control of networked autonomous air vehicles,” IEEE Transactions on Control Systems Technology, vol. 14, iss. 5, pp. 789-803, 2006.

[Bibtex]`@article{1668143, author={Finke, J. and Passino, K.M. and Sparks, Andrew G.}, journal={{IEEE} Transactions on Control Systems Technology}, title={Stable task load balancing strategies for cooperative control of networked autonomous air vehicles}, year={2006}, volume={14}, number={5}, pages={789-803}, abstract={We introduce a mathematical model for the study of cooperative control problems for multiple autonomous air vehicles (AAVs) connected via a communication network. We propose a cooperative control strategy based on task-load balancing that seeks to ensure that no vehicle is underutilized and we show how to characterize task-load balancing as a stability property. Then, using Lyapunov stability analysis, we provide conditions under which task-load balancing is achieved even in the presence of communication delays. Finally, we investigate performance properties of the cooperative controller using Monte Carlo simulations. This shows the benefits of cooperation and the effects of network delays and communication topology on performance}, keywords={Lyapunov methods;Monte Carlo methods;aircraft;cooperative systems;multi-robot systems;remotely operated vehicles;stability;Lyapunov stability analysis;Monte Carlo simulations;cooperative control strategy;distributed control;multiple autonomous air vehicles;task load balancing strategies;Communication networks;Communication system control;Delay effects;Load management;Lyapunov method;Mathematical model;Mobile robots;Network topology;Remotely operated vehicles;Stability;Cooperative systems;distributed control;distributed decision making;load balancing;mobile robots}, doi={10.1109/TCST.2006.876902}, ISSN={1063-6536} }`

### Conference Proceedings (Peer-reviewed)

- M. Romero, Ó. Ramírez, J. Finke, and C. Rocha, “Supervised Gene Function Prediction using Spectral Clustering on Gene Co-expression Networks,” in Proceedings of the International Conference on Complex Networks and their Applications, 2021.

[Bibtex]`@inproceedings{C001, author={Romero, Miguel and Ram{\'i}rez, {\'O}scar and Finke, Jorge and Rocha, Camilo}, booktitle={Proceedings of the International Conference on Complex Networks and their Applications}, title={Supervised Gene Function Prediction using Spectral Clustering on Gene Co-expression Networks}, year={2021}, abstract={Gene annotation addresses the problem of predicting unknown functions that are associated to the genes of a specific organism (e.g., biological processes). Despite recent advances, the cost and time demanded by annotation procedures that rely largely on in vivo biological experiments remain prohibitively high. This paper presents an in silico approach to the annotation of genes that follows a network-based representation, and combines techniques from multivariate statistics (spectral clustering) and machine learning (gradient boosting). Spectral clustering is used to enrich the gene co-expression network (GCN) with currently known gene annotations. Gradient boosting is trained on features of the GCN to build an estimator of the probability that a gene is involved in a given biological process. The proposed approach is applied to a case study on Zea mays, one of the world’s most dominant and productive crop. Broadly speaking, the main results illustrate how computational experimentation narrows down the time and costs in efforts to annotate the functions of genes. More specifically, the results highlight the importance of network science, multivariate statistics, and machine learning techniques in reducing types I and II prediction errors.} }`

- C. Pinzón, C. Rocha, and J. Finke, “Algorithmic Analysis of Blockchain Efficiency with Communication Delay,” in Fundamental Approaches to Software Engineering, Cham, 2020, p. 400–419.

[Bibtex]`@inproceedings{978-3-030-36687-2_53, author="Pinz{\'o}n, Carlos and Rocha, Camilo and Finke, Jorge", editor="Wehrheim, Heike and Cabot, Jordi", title="Algorithmic Analysis of Blockchain Efficiency with Communication Delay", booktitle="Fundamental Approaches to Software Engineering", year="2020", publisher="Springer International Publishing", address="Cham", pages="400--419", abstract={A blockchain is a distributed hierarchical data structure of blocks that has applications in various industries, e.g., in digital currencies such as bitcoin and ethereum. This paper proposes an algorithmic approach to the analysis of proof-of-work blockchain efficiency as a function of the total number of produced blocks and the average synchronization delay in the network. The algorithms and results are founded on a proposed random network model in which a tree of blocks grows adhering to a standard protocol. The network model is parametric on two probability distribution functions governing block production and communication delay. These distributions determine the synchronization efficiency of the distributed copies of the blockchain among the network workers and, therefore, are key for capturing the overall blockchain stochastic growth. The proposed algorithms are able to consider a fixed or an unbounded number of workers in the network, and are used for the analysis of different types of blockchain designs, e.g., systems in which the average time of block production can match the average time of message broadcasting required for synchronization. In particular, the proposed model and the algorithms provide insight into efficiency criteria for identifying conditions under which increasing block production has a negative impact on the stability of a blockchain, even before the system is deployed. Since the proposed model and algorithms are agnostic of the blockchain final use or its concrete implementation, they can serve as formal foundations for the modeling and analysis of a variety of non-functional properties of current and future blockchains.}, ISSN={1611-3349}, }`

- J. Campos and J. Finke, “Detecting Hotspots on Networks,” in Complex Networks and Their Applications VIII, Cham, Germany, 2020, p. 633–644.

[Bibtex]`@inproceedings{978-3-030-36687-2_53, author={Juan Campos and Jorge Finke}, editor={Cherifi, Hocine and Gaito, Sabrina and Mendes, Jos{\'e} Fernendo and Moro, Esteban and Rocha, Luis Mateus"}, title={Detecting Hotspots on Networks}, booktitle={Complex Networks and Their Applications VIII}, year={2020}, publisher={Springer International Publishing}, address={Cham, Germany}, pages={633--644}, abstract={Traditional approaches for measuring the concentration of events pay little attention to the effects of topological properties. To overcome this limitation, our work develops a theoretical framework to determine whether events are concentrated on a subset of interconnected nodes. We focus on low-clustered networks with regular, Poisson, and power-law degree distributions.}, ISSN={1860-949X}, }`

- J. Romero, J. Finke, and A. Salazar, “Fitness-weighted preferential attachment with varying number of new connections,” in Complex Networks and Their Applications VIII, Cham, Germany, 2020, p. 612–620.

[Bibtex]`@inproceedings{978-3-030-36687-2_51, author={Juan Romero and Jorge Finke and Andr\'es Salazar}, editor={Cherifi, Hocine and Gaito, Sabrina and Mendes, Jos{\'e} Fernendo and Moro, Esteban and Rocha, Luis Mateus"}, title={Fitness-weighted preferential attachment with varying number of new connections}, booktitle={Complex Networks and Their Applications VIII}, year={2020}, publisher={Springer International Publishing}, address={Cham, Germany}, pages={612--620}, abstract={Preferential attachment models are used to explain the emergence of power laws in the degree distributions of networks. These models assume that a new node attaches to a network by establishing edges to a fixed number of nodes. Nonetheless, for many empirical networks the number of new edges varies as more nodes become part of the network. This paper extends the linear preferential attachment model by considering that the number of new edges is characterized by a random variable that obeys a power law probability function. While most new nodes connect to a few nodes, some nodes connect to a larger number. We characterize the dynamics of growth of the degrees of the nodes and the degree distribution of the network.}, issn={978-3-030-36687-2_51}, }`

- M. Romero, C. Rocha, and J. Finke, “Spectral Evolution of Twitter Mention Networks,” in Complex Networks and Their Applications VIII, Cham, Germany, 2020, p. 532–542.

[Bibtex]`@inproceedings{978-3-030-36687-2_44, author={Miguel Romero and Camilo Rocha and Jorge Finke}, editor={Cherifi, Hocine and Gaito, Sabrina and Mendes, Jos{\'e} Fernendo and Moro, Esteban and Rocha, Luis Mateus"}, title={Spectral Evolution of Twitter Mention Networks}, booktitle={Complex Networks and Their Applications VIII}, year={2020}, publisher={Springer International Publishing}, address={Cham, Germany}, pages={532--542}, abstract="This papers applies the spectral evolution model presented to networks of mentions between Twitter users who identified messages with the most popular political hashtags in Colombia (during the period which concludes the disarmament of the Revolutionary Armed Forces of Colombia). The model characterizes the dynamics of each mention network (i.e., how new edges are established) in terms of the eigen decomposition of its adjacency matrix. It assumes that as new edges are established the eigenvalues change, while the eigenvectors remain constant. The goal of our work is to evaluate various link prediction methods that underlie the spectral evolution model. In particular, we consider prediction methods based on graph kernels and a learning algorithm that tries to estimate the trajectories of the spectrum. Our results show that the learning algorithm tends to outperform the kernel methods at predicting the formation of new edges.}`

- M. Romero, J. Finke, M. Quimbaya, and C. Rocha, “In-Silico Gene Annotation Prediction Using the Co-expression Network Structure,” in Complex Networks and Their Applications VIII, Cham, Germany, 2020, p. 802–812.

[Bibtex]`@inproceedings{978-3-030-36683-4_64, author={Miguel Romero and Jorge Finke and Mauricio Quimbaya and Camilo Rocha}, editor={Cherifi, Hocine and Gaito, Sabrina and Mendes, Jos{\'e} Fernendo and Moro, Esteban and Rocha, Luis Mateus"}, title={In-Silico Gene Annotation Prediction Using the Co-expression Network Structure}, booktitle={Complex Networks and Their Applications VIII}, year={2020}, publisher={Springer International Publishing}, address={Cham, Germany}, pages={802--812}, abstract={Identifying which genes are involved in particular biological processes is relevant to understand the structure and function of a genome. A number of techniques have been proposed that aim to annotate genes, i.e., identify unknown biological associations between biological processes and genes. The ultimate goal of these techniques is to narrow down the search for promising candidates to carry out further studies through in-vivo experiments. This paper presents an approach for the in-silico prediction of functional gene annotations. It uses existing knowledge body of gene annotations of a given genome and the topological properties of its gene co-expression network, to train a supervised machine learning model that is designed to discover unknown annotations. The approach is applied to Oryza Sativa Japonica (a variety of rice). Our results show that the topological properties help in obtaining a more precise prediction for annotating genes.}, issn={978-3-030-36683-4_64} }`

- K. Guerrero and J. Finke, “Dynamics of Group Cohesion in Homophilic Networks,” in Proceeding of the Conference on Decision and Control, Melbourne, Australia, 2017, pp. 2318-2323.

[Bibtex]`@inproceedings{8263988, author={Guerrero, K. and Finke, J.}, booktitle={Proceeding of the Conference on Decision and Control}, title={Dynamics of Group Cohesion in Homophilic Networks}, year={2017}, abstract={Understanding cohesion and homophily in empirical networks allows us build better personalization and recommendation systems. This paper proposes a network model that explains the emergence of cohesion and homophily as an aggregate outcome at the group- and network-level. We introduce two simple mechanisms that capture the underlying tendencies of nodes to connect with similar and different others. Our main theoretical result presents conditions on the network under which it reaches high degrees of cohesion and homophily.}, ISSN={0743-1546}, pages = {2318-2323}, address = {Melbourne, Australia}, }`

- Fernandez I., Passino K., and J. Finke, “Dynamics of Degree Distributions of Social Network,” in Proceeding of the Conference on Decision and Control, Melbourne, Australia, 2017, pp. 743-1546.

[Bibtex]`@inproceedings{8264406, author={Fernandez, I., and Passino, K., and Finke, J.}, booktitle={Proceeding of the Conference on Decision and Control}, title={Dynamics of Degree Distributions of Social Network}, year={2017}, abstract={Social network models aim to capture the complex structure of social connections. They are a framework for the design of control algorithms that take into account relationships, interactions, and communications between social actors. Based on three formation mechanisms - random attachment, triad formation, and network response - our work characterizes the dynamics of the degree distributions of social networks. In particular, we show that the complementary cumulative in- and out-degree distributions of highly clustered, reciprocal networks can be approximated by infinite dimensional time-varying linear systems. Furthermore, we determine the invariance of both limit distributions and the stability properties of the average degree.}, ISSN={0743-1546}, pages = {0743-1546}, address = {Melbourne, Australia}}`

- Romero J., Salazar A., and J. Finke, “Preferential attachment with power law growth in the number of new edges,” in Proceeding of the Conference on Decision and Control, Melbourne, Australia, 2017, p. 2680–2585.

[Bibtex]`@inproceedings{8264048, author={Romero, J., and Salazar, A., and Finke, J.}, booktitle={Proceeding of the Conference on Decision and Control}, title={Preferential attachment with power law growth in the number of new edges}, year={2017}, abstract={The Barabasi-Albert model is used to explain the formation of power laws in the degree distributions of networks. The model assumes that the principle of preferential attachment underlies the growth of networks, that is, new nodes connects to a fixed number of nodes with a probability that is proportional to their degrees. Yet, for empirical networks the number of new edges is often not constant, but varies as more nodes become part of the network. This paper considers an extension to the original Barabasi-Albert model, in which the number of edges established by a new node follows a power law distribution with support in the total number of nodes. While most new nodes connect to a few nodes, some new nodes connect to a larger number. We first characterize the dynamics of growth of the degree of the nodes. Second, we identify sufficient conditions under which the expected value of the average degree of the network is asymptotically stable. Finally, we show how the dynamics of the model resemble the evolution of protein interaction networks, Twitter, and Facebook.}, ISSN={0743-1546}, pages = {2680--2585}, address = {Melbourne, Australia}}`

- D. Ruiz and J. Finke, “Stability of the Jackson-Rogers model,” in Proceeding of the Conference on Decision and Control, Melbourne, Australia, 2017, p. 1803–1808.

[Bibtex]`@inproceedings{8263909, author={Ruiz, D. and Finke, J.}, booktitle={Proceeding of the Conference on Decision and Control}, title={Stability of the Jackson-Rogers model}, year={2017}, abstract={Network formation models explain the dynamics of the structure of connections using mechanisms that operate under different principles for establishing and removing edges. The Jackson-Rogers model is a generic framework that applies the principle of triadic closure to growing networks. Past work describes the asymptotic behavior of the degree distribution based on a continuous-time approximation. Here, we introduce a discrete-time approach that provides a more accurate fit of the dynamics of the in-degree distribution of the Jackson-Rogers model. Furthermore, we characterize the limit distribution and the expected value of the average degree as equilibria, and prove that both equilibria are asymptotically stable.}, ISSN={0743-1546}, pages = {1803--1808}, address = {Melbourne, Australia}}`

- J. Campos and J. Finke, “Anomalous Node Detection in Networks with Communities of Different Size,” in Proceeding of the American Control Conference, Seattle, WA, 2017, p. 3218 – 3223.

[Bibtex]`@inproceedings{07963443, author={Campos, J. and Finke, J.}, booktitle={Proceeding of the American Control Conference}, title={Anomalous Node Detection in Networks with Communities of Different Size}, year={2017}, abstract={Based on two simple mechanisms for establishing and removing links, this paper defines an event-driven model for the anomalous node detection problem. This includes a representation for (i) the tendency of regular nodes to connect with similar others (i.e., establish homophilic relationships); and (ii) the tendency of anomalous nodes to connect to random targets (i.e., establish random connections across the network). Our approach is motivated by the desire to design scalable strategies for detecting signatures of anomalous behavior, using a formal representation to take into account the evolution of network properties. In particular, we assume that regular nodes are distributed across two communities (of different size), and propose an algorithm that identifies anomalous nodes based on both geometric and spectral measures. Our focus is on defining the anomalous detection problem in a mathematical framework and to highlight key challenges when certain topological properties dominate the problem (i.e., in terms of the strength of communities and their size).}, ISSN={0743-1619}, pages = {3218 -- 3223}, address = {Seattle, WA}}`

- I. Fernandez and J. Finke, “Stability Properties of Reciprocal Networks,” in Proceeding of the American Control Conference, Boston, MA, 2016, p. 776–781.

[Bibtex]`@inproceedings{7525008, author={Fernandez, I. and Finke, J.}, booktitle={Proceeding of the American Control Conference}, title={Stability Properties of Reciprocal Networks}, year={2016}, abstract={Models of network formation explain key features of a wide class of empirical networks based on simple mechanisms for establishing and removing links. Such mechanisms include random attachment (a generic abstraction of how a new incoming node connects to a network), triad formation (how the new node establishes transitive relationships), and network response (how the network reacts to new attachments). Our work analyses the combined effect of these three mechanisms on various local and global connectivity properties. In particular, we derive an expression for the asymptotic behavior of the local reciprocity coefficient and show that the evolution of the global reciprocity and the global clustering coefficients satisfy the dynamics of a time-varying linear system. Furthermore, we identify conditions under which the equilibrium of the system is globally asymptotically stable. Finally, we calibrate the proposed model to capture the evolution of a network composed of e-mail messages sent between members of an online community of students from the University of California, Irvine.}, ISSN={0743-1619}, pages = {776--781}, address = {Boston, MA}}`

- I. Fernandez and J. Finke, “Transitivity of Reciprocal Networks,” in Proceeding of the Conference on Decision and Control, Osaka, Japan, 2015, p. 1625–1630.

[Bibtex]`@inproceedings{07402443, author={Fernandez, I. and Finke, J.}, booktitle={Proceeding of the Conference on Decision and Control}, title={Transitivity of Reciprocal Networks}, year={2015}, abstract={Network models are a useful tool to describe and predict dynamic relationships in large collections of data. Characterizing these relationships allows us to explain the emergence of underlying structures and estimate the expected degrees of uncertainty in connectivity patterns. This paper introduces an event-driven model that captures the effects of three simple network formation mechanisms: random attachment (a generic abstraction of how an incoming node connects to a network), triad formation (its impact on new neighbors), and network response (i.e., the way the overall network reacts to new attachments). Our work focuses on the impact of network response on the in- and out-degree distributions and the local clustering coefficients. In particular, we prove that under the proposed mechanisms any initial network reaches a stationary degree distribution with stationary clustering properties. Simulation results suggest that the response mechanism amplifies the scaling behavior of the degree distribution, originally induced by random attachment and triad formation.}, ISSN={0743-1546}, pages = {1625--1630}, address = {Osaka, Japan}}`

- P. Moriano and J. Finke, “Model-based Fraud Detection in Growing Networks,” in Proceeding of the Conference on Decision and Control, Los Angeles, CA, 2014, pp. 6068-6073.

[Bibtex]`@inproceedings{7040339, author={Moriano, P. and Finke, J.}, booktitle={Proceeding of the Conference on Decision and Control}, title={Model-based Fraud Detection in Growing Networks}, year={2014}, pages={6068 - 6073}, abstract={People share opinions, exchange information, and trade services on large, interconnected platforms. Because of the size of these platforms, they are common targets for fraudsters who try to deceive randomly selected users. To monitor such behavior, the proposed algorithm evaluates anomalies in the network structure that results from local interactions between users. In particular, the algorithm evaluates the degree of membership to well-defined communities of users and the formation of close-knit groups in their neighborhoods. We identify a set of suspects using a first order approximation of the evolution of the eigenpairs associated to the network; and within the set of suspects, we locate fraudsters based on deviations from the expected local clustering coefficients. Simulations illustrate how incorporating structural properties (their asymptotic behavior) into the design of the algorithm allows us to differentiate between the aggregate dynamics of fraudsters and regular users.}, ISSN={0743-1546}, address = {Los Angeles, CA}}`

- I. Fernandez and J. Finke, “On the stability of resource undermatching in human group-choice,” in Proceedings of the American Control Conference, Portland, OR, 2014, pp. 2059-2064.

[Bibtex]`@inproceedings{6858908, author={Fernandez, I. and Finke, J.}, booktitle={Proceedings of the American Control Conference}, title={On the stability of resource undermatching in human group-choice}, year={2014}, pages={2059-2064}, abstract={The analysis of patterns of social interaction plays an important role in providing services on online platforms (e.g., in designing algorithms for the allocation of information resources). The proposed model takes into account human factors underlying the concept of the Ideal Free Distribution (IFD), which captures empirical patterns of the aggregate group-level behavior of individuals competing for resources. The model explains the phenomenon of resource undermatching as a natural IFD-based outcome resulting from boundedly rational decision-making (i.e., individuals perceive only some of the available resources). We show that undermatching can be described as a globally balanced state in which the perceived cost of the best forgone alternatives is approximately the same for all individuals. Furthermore, we identify conditions that guarantee stability. From this analysis, we infer that the matching of the aggregate of individual choices to resources is independent of their initial distribution. Finally, we quantify the effect of resource scarcity on the degree of matching.}, keywords={behavioral sciences;decision making;human factors;IFD;dynamic model;empirical dispersal patterns;human decision-making;human factors;ideal free distributions;large-scale distributed applications;population-dependent equilibrium point;uncertainty reduction;Asymptotic stability;Decision making;Human factors;Nickel;Sensitivity;Stability criteria}, ISSN={0743-1619}, address = {Portland, OR}}`

- P. Moriano and J. Finke, “Characterizing the relationship between degree distributions and community structures,” in Proceedings of the American Control Conference, Portland, OR, 2014, pp. 2383-2388.

[Bibtex]`@inproceedings{6858882, author={Moriano, P. and Finke, J.}, booktitle={Proceedings of the American Control Conference}, title={Characterizing the relationship between degree distributions and community structures}, year={2014}, pages={2383-2388}, abstract={Extended power laws and inhomogeneous connections are structural patterns often found in empirical networks. Mechanisms based on the formation of triads are able to explain the power law behavior of the degree distribution of such networks. The proposed model introduces a two-step mechanism of attachment and triad formation that illustrates how preferential linkage plays an important role in shaping the inhomogeneity of connections and the division of the network into groups of nodes (i.e., the growth of community structures). In particular, we identify conditions under which the scaling exponent of the power law correlates to a widely-used modularity measure of non-overlapping communities. Our analytical results characterize the asymptotic behavior of both the scaling exponent and the modularity, as a function of the strength with which nodes with similar characteristics tend to link to each other.}, keywords={pattern clustering;probability;extended power law behavior;growing network structure;in-degree distribution;link formation;preferential attachment principle;strong neighborhood clustering;triad junction formation probability;Analytical models;Indexes;Junctions;Patents;Probability distribution;Random processes;Random variables}, ISSN={0743-1619}, address = {Portland, OR}}`

- D. Ruiz and J. Finke, “Invalidation of dynamic network models,” in Proceedings of the American Control Conference, Washington, DC, 2013, pp. 138-143.

[Bibtex]`@inproceedings{6579827, author={Ruiz, D. and Finke, J.}, booktitle={Proceedings of the American Control Conference}, title={Invalidation of dynamic network models}, year={2013}, pages={138-143}, abstract={Models of discrete event systems combine ideas from control theory and computer science to represent the evolution of distributed processes. We formalize a notion of the invalidation of models presumed to describe dynamics on networks, and introduce an algorithm to evaluate a class of event-driven processes that evolve close to an invariant and stable state. The algorithm returns the value true, if according to the proposed notion of invalidation, the evolution of empirical observations is inconsistent with the stability properties of the model. To illustrate the approach, we represent a generic decision-making process in which the marginal utility of allocating agents to particular nodes rests on the well-known concept in economy theory of the law of diminishing returns.}, keywords={discrete event systems;stability;discrete event system;distributed process;dynamic network model;event-driven process;generic decision-making process;marginal utility;stability property;Biological system modeling;Discrete-event systems;Heuristic algorithms;Resource management;Stability analysis;Trajectory;Vectors}, ISSN={0743-1619}, address = {Washington, DC}}`

- I. Fernandez, J. Finke, and D. Ruiz, “Ideal Free Distributions in human decision-making,” in Proceedings of the American Control Conference, Washington, DC, 2013, pp. 917-922.

[Bibtex]`@inproceedings{6579953, author={Fernandez, I. and Finke, J. and Ruiz, D.}, booktitle={Proceedings of the American Control Conference}, title={Ideal Free Distributions in human decision-making}, year={2013}, pages={917-922}, abstract={Integrating human factors into the design of large-scale distributed applications requires capturing broad patterns of decision-making over time. The proposed theoretical framework introduces a dynamic model that resembles empirical dispersal patterns between the quality of an option and the number of individuals choosing that option. We use the notion of the Ideal Free Distribution (IFD) to estimate the resulting population-dependent equilibrium point and reduce uncertainty about how groups of individuals choose between available options. Our contribution is twofold. First, we identify conditions that lead to the IFD under constrained choice. Second, we illustrate how biases in decision-making can lead to systemic deviations from the IFD.}, keywords={behavioural sciences;decision making;human factors;IFD;dynamic model;empirical dispersal patterns;human decision-making;human factors;ideal free distributions;large-scale distributed applications;population-dependent equilibrium point;uncertainty reduction;Asymptotic stability;Decision making;Human factors;Nickel;Sensitivity;Stability criteria}, ISSN={0743-1619}, address = {Washington, DC}}`

- P. Moriano and J. Finke, “Structure of growing networks with no preferential attachment,” in Proceedings of the American Control Conference, Washington, DC, 2013, pp. 1088-1093.

[Bibtex]`@inproceedings{6579981, author={Moriano, P. and Finke, J.}, booktitle={Proceedings of the American Control Conference}, title={Structure of growing networks with no preferential attachment}, year={2013}, pages={1088-1093}, abstract={Based on the formation of triad junctions, the proposed mechanism generates growing networks that exhibit extended power law behavior and strong neighborhood clustering. The asymptotic behavior of both properties is of interest in the study of networks in which (i) the formation of links cannot be described according to the principle of preferential attachment; (ii) the in-degree distribution fits a power law for nodes with a high degree and an exponential form otherwise; and (iii) the degree of clustering depends on both the number of links that newly added nodes establish and the probability of forming triads.}, keywords={pattern clustering;probability;extended power law behavior;growing network structure;in-degree distribution;link formation;preferential attachment principle;strong neighborhood clustering;triad junction formation probability;Analytical models;Indexes;Junctions;Patents;Probability distribution;Random processes;Random variables}, ISSN={0743-1619}, address = {Washington, DC}}`

- J. M. Nogales and J. Finke, “Optimal distribution of heterogeneous agents under delays,” in Proceedings of the American Control Conference, Washington, DC, 2013, pp. 3212-3217.

[Bibtex]`@inproceedings{6580326, author={Nogales, J.M. and Finke, J.}, booktitle={Proceedings of the American Control Conference}, title={Optimal distribution of heterogeneous agents under delays}, year={2013}, pages={3212-3217}, abstract={An analytical framework for the study of a generic distribution problem is introduced in which a group of agents with different capabilities intend to maximize total utility by dividing themselves into various subgroups without any form of global information-sharing or centralized decision-making. The marginal utility of belonging to a particular subgroup rests on the well-known concept in economic theory of the law of diminishing returns. For a class of discrete event systems, we identify a set of conditions that define local information and cooperation requirements, and prove that if the proposed conditions are satisfied a stable agent distribution representing a Pareto optimum is achieved even under random but bounded decision and transition delays.}, keywords={Pareto analysis;decision making;multi-agent systems;Pareto optimum;centralized decision making;economic theory;generic distribution problem;global information sharing;optimal heterogeneous agent distribution;stable agent distribution;transition delays;Decision making;Delays;Discrete-event systems;Economics;Indexes;Sensors;Vectors}, ISSN={0743-1619}, address = {Washington, DC}}`

- K. Guerrero and J. Finke, “On the formation of community structures from homophilic relationships,” in Proceedings of the American Control Conference, Montreal, QC, 2012, pp. 5318-5323.

[Bibtex]`@inproceedings{6315325, author={Guerrero, K. and Finke, J.}, booktitle={Proceedings of the American Control Conference}, title={On the formation of community structures from homophilic relationships}, year={2012}, pages={5318-5323}, abstract={Many real-world networks consist of numerous interconnected groups which, as communities, display distinctive collective behavior. The division of a network into communities-groups of nodes with a high density of ties within but a low density of ties between groups- underlies the structure of social and technological networks. In human communities, for instance, individuals may group together according to special interest, occupation, intent, or belief, with tendency to establish stronger ties with individuals who are similar to themselves. Here, we introduce a formal framework for the formation of community structures from homophilic relationships between individuals. Stochastic modeling of local relationships allows us to identify a wide class of agent interactions which lead to the formation of communities and quantify the extent to which group size affects the resulting structure.}, keywords={group theory;network theory (graphs);stochastic processes;agent interactions;community structure formation;homophilic relationships;human communities;interconnected groups;real-world networks;social network structure;technological network structure;Communities;Decision making;Indexes;Joining processes;Markov processes;Transient analysis}, ISSN={0743-1619}, address = {Montreal, QC}}`

- J. M. Nogales and J. Finke, “Local requirements for optimal distribution of heterogeneous agents,” in Proceeding of the Conference on Decision and Control and the European Control Conference, Orlando, FL, 2011, pp. 3596-3601.

[Bibtex]`@inproceedings{6161376, author={Nogales, J.M. and Finke, J.}, booktitle={Proceeding of the Conference on Decision and Control and the European Control Conference}, title={Local requirements for optimal distribution of heterogeneous agents}, year={2011}, pages={3596-3601}, abstract={This paper introduces an analytical framework for the study of a generic distribution problem where a group of heterogeneous agents intend to divide themselves into various subgroups without any form of global information-sharing or centralized decision-making. Subgroups are associated to mathematical functions that capture the marginal utilities of performing tasks, each satisfying the law of diminishing returns. We prove that under generic local requirements a stable agent distribution representing a Nash equilibrium can be achieved, and show via Monte Carlo simulations how the proposed set of rules performs under varying constraints on information flow and degrees of cooperation.}, keywords={Monte Carlo methods;functions;game theory;system theory;Monte Carlo simulations;Nash equilibrium;cooperation degree;diminishing returns law;generic distribution problem;generic local requirements;information flow;marginal utilities;mathematical functions;optimal heterogeneous agent distribution;stable agent distribution;subgroups;Companies;Indexes;Measurement;Monte Carlo methods;Nash equilibrium;Vectors}, ISSN={0743-1546}, address = {Orlando, FL}}`

- P. Moriano and J. Finke, “Heavy-tailed weighted networks from local attachment strategies,” in Proceeding of the Conference on Decision and Control and the European Control Conference, Orlando, FL, 2011, pp. 5211-5216.

[Bibtex]`@inproceedings{6161494, author={Moriano, P. and Finke, J.}, booktitle={Proceeding of the Conference on Decision and Control and the European Control Conference}, title={Heavy-tailed weighted networks from local attachment strategies}, year={2011}, pages={5211-5216}, abstract={Large networks arise by the gradual addition of nodes attaching to an existing and evolving network component. There are a wide class of attachment strategies which lead to distinct structural features in growing networks. This paper introduces a mechanism for constructing, through a process of distributed decision-making, substrates for the study of collective dynamics on power-law weighted networks with both a desired scaling exponent and a desired clustering coefficient. The analytical results show that the connectivity distribution converges to the scaling behavior often found in social and engineering systems. To illustrate the approach of the proposed framework we generate network substrates that resemble the empirical citation distributions of (i) patents granted by the U.S. Patent and Trademark Office from 1975 to 1999; and (ii) opinions written by the Supreme Court and the cases they cite from 1754 to 2002.}, keywords={citation analysis;decision making;information networks;network theory (graphs);clustering coefficient;connectivity distribution;distributed decision making;empirical citation distribution;engineering system;heavy-tailed weighted network component;local attachment strategy;power law weighted network substrate;scaling behavior;social system;Asymptotic stability;Complex networks;Conferences;Mathematical model;Patents;Probability distribution;Trajectory}, ISSN={0743-1546}, address={Orlando, FL}}`

- J. Finke, B. J. Moore, and K. M. Passino, “Stable emergent agent distributions under sensing and travel delays,” in Proceeding of the Conference on Decision and Control, Cancun, Mexico, 2008, pp. 1809-1814.

[Bibtex]`@inproceedings{4738797, author={Finke, J. and Moore, B.J. and Passino, K.M.}, booktitle={Proceeding of the Conference on Decision and Control}, title={Stable emergent agent distributions under sensing and travel delays}, year={2008}, pages={1809-1814}, abstract={In order for a team of cooperating agents to achieve a group goal (such as searching for targets, monitoring an environment, etc.) those agents must be able to share information and achieve some level of coordination. Since realistic methods of communication between agents have limited range and speed, the agentsÃ‚Â¿ decision-making strategies must operate with incomplete and outdated information. Moreover, in many situations the agents must travel to particular locations in order to perform various tasks, and so there will also be a delay between any particular decision and its effect. In this paper we develop an asynchronous framework that models the behavior of a group of agents that is spatially distributed across a predefined area of interest. We derive general conditions under which the group is guaranteed to converge to a specific distribution within the environment without any form of central control and despite unknown but bounded delays in sensing and travel. The achieved distribution is optimal in the sense that the proportion of agents allocated over each area matches the relative importance of that area. Finally, based on the derived conditions, we design a cooperative control scheme for a multi-agent surveillance problem. Via Monte Carlo simulations we show how sensing and travel delays and the degree of cooperation between agents affect the rate at which they achieve the desired coverage of the region under surveillance.}, keywords={Monte Carlo methods;control engineering computing;cooperative systems;decision making;delay systems;multi-robot systems;Monte Carlo simulations;agent distributions;asynchronous framework;cooperating agents;cooperative control scheme;decision-making strategies;travel delays;Centralized control;Communication system control;Computerized monitoring;Control systems;Cooperative systems;Decision making;Delay effects;Distributed computing;Distributed decision making;Surveillance}, ISSN={0191-2216}, address={Cancun, Mexico}}`

- J. Finke, N. Quijano, and K. M. Passino, “Ideal Free Distributions in growing networks,” in Proceeding of the American Control Conference, Seattle, WA, 2008, pp. 159-164.

[Bibtex]`@inproceedings{4586484, author={Finke, J. and Quijano, N. and Passino, K.M.}, booktitle={Proceeding of the American Control Conference}, title={{Ideal Free Distributions} in growing networks}, year={2008}, pages={159-164}, abstract={This paper presents a class of network optimization processes that account for the emergence of scale-free network structures. We introduce a mathematical framework that captures the connectivity and growth dynamics of a network with an arbitrary initial topology. We show how selection via differential node fitness affects the proportion of connections a node makes to other nodes, and how a heavy-tailed connectivity behavior manifests itself from consecutive achievements of ideal free distributions (IFDs). Finally, we present simulation results that show how this class of networks may emerge even when consecutive IFDs are not perfectly reached.}, keywords={complex networks;graph theory;network theory (graphs);optimisation;statistical distributions;graph theory;heavy-tailed connectivity behavior;ideal free distribution;mathematical framework;network growth dynamics;network optimization;scale-free network structures;topology;Acceleration;Collaborative work;Computational modeling;Computer networks;Equations;Interconnected systems;Network topology;Networked control systems;Proteins;Web sites}, ISSN={0743-1619}, address={Seattle, WA}}`

- J. Finke and K. M. Passino, “Stable emergent heterogeneous agent distributions in noisy environments,” in American Control Conference, Minneapolis, MN, 2006, p. 6–14.

[Bibtex]`@inproceedings{1656534, author={Finke, J. and Passino, K.M.}, booktitle={American Control Conference}, title={Stable emergent heterogeneous agent distributions in noisy environments}, year={2006}, pages={6--14}, abstract={A mathematical model is introduced for the study of the behavior of a spatially distributed group of heterogenous agents which possess noisy assessments of the state of their immediate surroundings. We define general sensing and motion conditions on the agents that guarantee the emergence of a type of "ideal free distribution" (IFD) across the environment, and focus on how individual and environmental characteristics affect this distribution. In particular, we show the impact of the agents' maneuvering and sensing abilities for different classes of environments, and how spatial constraints of the environment affect the rate at which the distribution is achieved. Finally, we apply this model to a cooperative vehicle control problem and present simulation results that show the benefits of an IFD-based distributed decision-making strategy}, keywords={Lyapunov methods;multi-agent systems;stability;vehicles;agent maneuvering;agent sensing;cooperative vehicle control;decision-making strategy;ideal free distribution;mathematical model;noisy environments;spatial constraints;spatially distributed group;stable emergent heterogeneous agent distributions;Animals;Biological system modeling;Collaborative work;Costs;Distributed decision making;Environmental factors;Mathematical model;Topology;Vehicle dynamics;Working environment noise}, address={Minneapolis, MN}}`

- J. Finke and K. M. Passino, “Stable cooperative multiagent spatial distributions,” in Proceeding of the Conference on Decision and Control and the European Control Conference, Sevilla, Spain, 2005, pp. 3566-3571.

[Bibtex]`@inproceedings{1582715, author={Finke, J. and Passino, K.M.}, booktitle={Proceeding of the Conference on Decision and Control and the European Control Conference}, title={Stable cooperative multiagent spatial distributions}, year={2005}, pages={3566-3571}, abstract={This paper introduces a mathematical model of the behavior of a group of agents and their interactions in a shared environment. We represent environmental spatial constraints that allow us to model range-limited sensing, motion, and communication capabilities of the agents. We derive general sensing, coordination, and motion conditions on the agents that guarantee that an "ideal free distribution" (IFD) of the group of agents will emerge across the environment. We show the impact of group size on the distribution of agents, and consider the emergent distribution for different classes of environments. Finally, we show how this theory is useful in solving a multivehicle cooperative surveillance problem.}, keywords={Animals;Collaborative work;Costs;Environmental factors;Interference;Mathematical model;Nash equilibrium;Sparks;Surveillance;Topology}, address={Sevilla, Spain}}`

- J. Finke, K. M. Passino, and A. Sparks, “Cooperative control via task load balancing for networked uninhabited autonomous vehicles,” in Proceedings of the Conference on Decision and Control, Maui, HI, 2003, pp. 31-36.

[Bibtex]`@inproceedings{1272531, author={Finke, J. and Passino, K.M. and Sparks, A.}, booktitle={Proceedings of the Conference on Decision and Control}, title={Cooperative control via task load balancing for networked uninhabited autonomous vehicles}, year={2003}, volume={1}, pages={31-36}, abstract={In this paper we first define a mathematical model of the "plant" for the cooperative control problem for multiple uninhabited autonomous vehicles (UAVs). This includes a representation for the vehicles, environment, and communication network. Next, we define an approach to cooperative control that uses local UAV task planning and multi-UAV coordination via task load balancing over the communication network. Our approach is motivated by our desire to cope with significant imperfections in the communication network and uncertainty in the environment, and yet provide a scalable strategy which can be implemented in real time across a network of UAVs, each of which only has relatively low processing power. Our focus in this paper is on defining the cooperative controller problem in a mathematical framework that is familiar to a control engineer, studying properties of a load balancing strategy for cooperation, and then via simulations to identify key challenges when network influences dominate the problem.}, keywords={mobile robots;multi-robot systems;remotely operated vehicles;resource allocation;communication network;cooperative control;load balancing strategy;mobile robots;multiple uninhabited autonomous vehicles;multirobot systems;networked uninhabited autonomous vehicles;simulation;task planning;uncertainty environments;Communication networks;Communication system control;Delay effects;Load management;Mathematical model;Mobile robots;Remotely operated vehicles;Sparks;Uncertainty;Unmanned aerial vehicles}, ISSN={0191-2216}, address={Maui, HI}}`

### Book Chapters

- J. Finke, K. M. Passino, S. Ganapathy, and A. Sparks, “Modeling and analysis of cooperative control systems for uninhabited autonomous vehicles,” in Cooperative Control, V. Kumar, N. Leonard, and A. Morse, Eds., Springer Berlin Heidelberg, 2005, vol. 309, pp. 79-102.

[Bibtex]`@incollection{978-3-540-31595-7_5, year={2005}, isbn={978-3-540-22861-5}, booktitle={Cooperative Control}, volume={309}, series={Lecture Notes in Control and Information Science}, editor={Kumar, Vijay and Leonard, Naomi and Morse, A.Stephen}, doi={10.1007/978-3-540-31595-7_5}, title={Modeling and analysis of cooperative control systems for uninhabited autonomous vehicles}, url={http://dx.doi.org/10.1007/978-3-540-31595-7_5}, publisher={Springer Berlin Heidelberg}, author={Finke, Jorge and Passino, Kevin M. and Ganapathy, Sriram and Sparks, Andrew}, pages={79-102},}`

- J. Finke and K. Passino, “The ecological Ideal Free Distribution and distributed networked control systems,” in Unifying Themes in Complex Systems, A. Minai, D. Braha, and Y. Bar-Yam, Eds., Springer Berlin Heidelberg, 2011, pp. 128-135.

[Bibtex]`@incollection{978-3-642-17635-7_16, year={2011}, isbn={978-3-642-17634-0}, booktitle={Unifying Themes in Complex Systems}, editor={Minai, AliA. and Braha, Dan and Bar-Yam, Yaneer}, doi={10.1007/978-3-642-17635-7_16}, title={The ecological {Ideal Free Distribution} and distributed networked control systems}, url={http://dx.doi.org/10.1007/978-3-642-17635-7_16}, publisher={Springer Berlin Heidelberg}, author={Finke, Jorge and Passino, KevinM.}, pages={128-135}, language={English},}`

### Other Publications

- J. V. Barr, E. M. Pinilla, and J. Finke, “A legal perspective on the use of models in the fight against corruption,” South Carolina Journal of International Law and Business, vol. 8, iss. 2, pp. 267-296, 2012.

[Bibtex]`@article{822012, author={J. V. Barr and E. M. Pinilla and J. Finke}, title={A legal perspective on the use of models in the fight against corruption}, journal={South Carolina Journal of International Law and Business}, volume={8}, number={2}, pages={267-296}, url={http://scholarcommons.sc.edu/scjilb/vol8/iss2/5}, year={2012}, }`

- P. Morales and J. Finke, “Small-World networks of corruption,” CIFE: Lecturas de Economía Social, vol. 17, iss. 26, pp. 19-36, 2015.

[Bibtex]`@article{822013, author={P. Morales and J. Finke}, title={Small-World networks of corruption}, journal={CIFE: Lecturas de Econom\'ia Social}, volume={17}, number={26}, pages={19-36}, year={2015}, abstract={Collective behavior forms and spreads through social contact. This thesis introduces a framework for understanding how the structure of social ties may impact the evolution of bribery. We represent relationships as highly clustered networks with small characteristic path lengths (i.e., small-world models having “local” and “long-range” contacts). Based on a principal-agent-client formulation, our model focuses on the effects of clustering on an equilibrium of persistent bribery. Collective outcomes depend on decision-making mechanisms that rely on sensitivity functions, which capture the level of influence between local contacts. Moreover, we represent the evolution of the network as a system of differential equations and identify its region of parameters for which the equilibrium of persistent bribery is stable. Our results show that an increase in clustering tends to decrease the levels of bribery. A more sensitive response to the behavior of neighbors, on the other hand, tends to increase bribery, but only up to a certain point. Beyond this threshold, the expected level of bribery remains constant, despite variations in the structural properties of the network} }`

- P. Moriano, J. Finke, and Y. -Y. Ahn, “Community-based anomalous event detection in temporal networks,” in Conference on Complex Systems, Cancun, Mexico, 2017.

[Bibtex]`@inproceedings{822014, author={P. Moriano and J. Finke and Y.-Y Ahn}, title={Community-based anomalous event detection in temporal networks}, booktitle ={Conference on Complex Systems}, month={September}, address={Cancun, Mexico}, year={2017}, abstract={Communication networks exhibit community structure based on demographic, geographic, and topical homophily. Members of each community tend to have common interests and to share most contents primarily within their community, exhibiting behavioral characteristics of complex contagion. By contrast, previous studies have shown that viral contents tend to behave like simple contagion, easily crossing community boundaries. We hypothesize that contents about an important event tend to be viral because they are relevant to a large fraction of people in the network, and that the increase in viral contents due to the event will trigger more communication across existing communities. We conrm our hypothesis and demonstrate that it can be used to detect anomalous events from temporal communication network structure, namely by monitoring and comparing the communication volume within and across communities. We use two examples|the collapse of Enron and the Boston Marathon bombing|to show that the communication volume across communities indeed increases when the information about the events was spreading across the communication network.} }`