#18.05 - Dynamic topic analysis in networks with text

• M. Corneli (UCA - Lab Dieudonné): "A dynamic stochastic topic block model for networks with textual edges"

The increasing volume of communication in social networks (e.g. Linkedin, Twitter and Facebook), personal emails (Gmail, Clinton’s mails, ...), emails of companies (e.g. Enron emails), digital/numeric documents (Panama papers, co-authorships, ...), or archived documents in libraries (digital humanities) has recently given rise to new techniques that account for homogeneous groups within a given network in terms of graph connectivity as well as - and that is new - the textual content of the edges. Dynamic extensions of these approches try to detect structural changes in the networks (nodes may come and go – edges may crash and recover) that can affect either the groups composition or the way existing groups interact.

Based on the STBM (stochastic topic block model), a probabilistic model is developed to cluster the nodes of a dynamic graph, accounting for the content of textual edges as well as their frequency. Vertices are grouped together such that they are homogeneous in terms of interaction frequency and the discussed topics. The dynamic graph is considered stationary within a time interval if the proportions of topics discussed between each pair of node groups do not change during that interval.

Experiments on simulated data and an application to the Enron dataset assess and illustrate the proposed approach.

Paper:
Corneli, Bouveyron, Latouche & Rossi (2018): "The dynamic stochastic topic block model for dynamic networks with textual edges"