Model-based fraud detection in growing networks
This notebook illustrates the dynamics of a model that captures the average behavior of regular users. Based on the asymptotic behavior of the local clustering, our paper proposes an algorithms that evaluates the degree of membership of each user to well-defined communities, as well as to close-knit groups. The video below shows how communities are formed.
Under the assumption that fraudsters engage in deceptive transactions in a way that resembles random link attacks, the resulting dynamics are shown below.
Structure of growing networks with no preferential attachment
This notebook illustrates the asymptotic behavior of the degree distribution for highly clustered networks. In particular, it tries to explain the formation of structural properties under the two following conditions:
- The formation of links cannot be described according to the principle of preferential attachment; and
- The in-degree distribution fits a power law for nodes with a high degree and an exponential form otherwise (i.e., an extended power).
Extended power laws are – in some contexts – a better fit than single or double power laws, e.g., to describe the degree distribution of online social networks and patent citation networks. Our paper characterizes the relation between the scaling exponent and the probability of forming triads. The transition from exponential to power law distributions depends on both the scaling exponent and the number of links that newly added nodes establish. Note that the underlying mechanism accounts for strong neighborhood clustering based on a random triad formation process. Average clustering properties remain constant as the size of the network grows. To download the notebook click here.