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CLASSIFICATION AUTOMATIQUE DE RÉSEAUX DYNAMIQUES AVEC SOUS-GRAPHES : ÉTUDE DU SCANDALE ENRON

Abstract : Abstract. — In recent years, many random graph models have been proposed to extract information from networks. The principle is to look for com-munities or groups of vertices with homogenous connection profiles. Most of these models are suitable for static networks, that is to say, not taking into account the temporal dimension, but can handle different types of edges, whether binary or discrete. This work is motivated by the need of analysing an evolving network 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 pro-posed 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 networks. We call the latter the dynamic random subgraph model (dRSM). A variational expectation maximisation (VEM) algorithm is proposed to perform inference. We show that the variational approximations lead to a new state space model from which the parameters along with hidden states can be estimated using the standard Kalman filter and Rauch-Tung-Striebel (RTS) smoother. The me-thodology is finally applied to the Enron email dataset and allows to discover a early reaction of the partners and directors compared to the other employees regarding the coming scandal.
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https://hal.archives-ouvertes.fr/hal-01086633
Contributor : Rawya Zreik <>
Submitted on : Wednesday, May 20, 2015 - 3:02:39 PM
Last modification on : Sunday, March 29, 2020 - 5:42:15 PM
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Rawya Zreik, Pierre Latouche, Charles Bouveyron. CLASSIFICATION AUTOMATIQUE DE RÉSEAUX DYNAMIQUES AVEC SOUS-GRAPHES : ÉTUDE DU SCANDALE ENRON. Journal de la Société Française de Statistique, Société Française de Statistique et Société Mathématique de France, 2015, pp.30. ⟨hal-01086633v2⟩

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