Modelling community structure and temporal spreading on complex networks

Abstract We present methods for analysing hierarchical and overlapping community structure spreading phenomena on complex networks. Different models can be developed describing static connectivity or dynamical processes a network topology. In this study, classical influence are used as examples models. Analysis of results is based probability matrix interactions between all pairs nodes in the network. One popular research area has been detecting communities their The detection method study optimising quality function calculated from matrix. same proposed underlying groups that building blocks different sub-communities structure. quantitative measures comparing ranking solutions algorithm. These describe properties sub-communities: strength community, formation robustness composition. main contribution proposing common methodology dynamics illustrate with two small topologies. case models, time development studied. Two temporal distributions demonstrate three real-world social networks sizes. Poisson distribution describes random response e-mail forwarding process receiving messages.