The stochastic topic block model for the clustering of vertices in networks with textual edges

Charles Bouveyron (1) , Pierre Latouche (2) , Rawya Zreik (1)

1 MAP5 - UMR 8145 - Mathématiques Appliquées Paris 5
2 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)

Due to the significant increase of communications between individuals via social media (Face-book, Twitter, Linkedin) or electronic formats (email, web, e-publication) in the past two decades, network analysis has become a unavoidable discipline. Many random graph models have been proposed to extract information from networks based on person-to-person links only, without taking into account information on the contents. This paper introduces the stochastic topic block model (STBM), a prob-abilistic model for networks with textual edges. We address here the problem of discovering meaningful clusters of vertices that are coherent from both the network interactions and the text contents. A classification variational expectation-maximization (C-VEM) algorithm is proposed to perform inference. Simulated data sets are considered in order to assess the proposed approach and to highlight its main features. Finally, we demonstrate the effectiveness of our methodology on two real-word data sets: a directed communication network and a undirected co-authorship network.

Mots clés: Random graph models; topic modeling; textual edges; clustering; variational inference

Domaines: Statistiques [math.ST]

Charles Bouveyron, Pierre Latouche, Rawya Zreik. The stochastic topic block model for the clustering of vertices in networks with textual edges. Statistics and Computing, 2016, ⟨10.1007/s11222-016-9713-7⟩. ⟨hal-01519743⟩