Introduction; empirical studies; real networks are not random.
This lecture presents formal methods to quantify the dynamics of corruption through modeling basic features of the structure and function of complex networks. It overviews the statistical way of thinking about social and technological networks.
Instructor: Jorge Finke
Office hours: we 11:00-1:00 p.m.
|1||Lec 1 - Introduction to information networks|
|2||Lec 2 - Common structural properties (part 1)|
|3||Lec 3 - Common structural properties (part 2)|
|4||Lec 4 - Common structural properties (part 3)|
|5||Lec 5 - Visualization tools|
|6||Lec 6 - Generalized random graphs|
|7||Lec 7 - Small-world graphs|
|8||Lec 8 - Scale-free networks|
|9||Lec 9 - Models of network growth|
|10||Lec 10 - Percolation theory and network dynamics|
|11||Lec 11 - Epidemic processes on networks|
|12||Lec 12 - Dynamics of corruption|
|13||Lec 13 - Course review|
Common network properties (part 1): small-world effect; transitivity or clustering; degree distribution; network resilience.
Common network properties (part 2): small-world effect; transitivity or clustering; network resilience; mixing patterns.
Common network properties (part 3): mixing patterns (scalar properties); degree correlation; community structure.
Network visualization tools; mathematical models of networks (part 1): poisson random graphs.
Generalized random graphs.
Small-world graphs; effect of small-world properties on dynamics (e.g., spread of corruption).
Price’s model; master-equation (rate-equation) method
Dynamic processes on networks (part 1); percolation theory; network failure.
Dynamics on networks (part 2); The SIR and SIS model.
Dynamic processes on networks (part 2); epidemics of corruption.