A State-Space Model for the Dynamic Random Subgraph Model
Rawya Zreik, Pierre Latouche, Charles Bouveyron, Rawya Zreik, Pierre Latouche, Charles Bouveyron · 2015
Abstract. In recent years, many random graph models have been pro-posed to extract information from networks. The principle is to look for groups of vertices with homogenous connection profiles. Most of these models are suitable for static networks and can handle different types of edges. This work is motivated by the need of analyzing an evolving net-work describing email communications between employees of the Enron compagny where social positions play an important role. Therefore, in this paper, we consider the random subgraph model (RSM) which was proposed recently to model networks through latent clusters built within known partitions. Using a state space model to characterize the cluster proportions, RSM is then extended in order to deal with dynamic net-works. We call the latter the dynamic random subgraph model (dRSM). 1