Charles Bouveyron先生のご講演 (Institut 3IA Côte d’Azur所長，INRIA-MAASAIチームリーダー) [深層学習理論チーム]

講演者：Charles Bouveyron (https://math.univ-cotedazur.fr/~cbouveyr)
日時： 2024年7月29日 15:00--16:00
場所：東京大学本郷キャンパス工学部６号館３階セミナー室A-D

講演題目：
Deep latent variable models for the analysis and clustering of networks and texts

講演概要：
Numerical interactions leading to users sharing textual content published by others are naturally represented by a network where the individuals are associated with the nodes and the exchanged texts with the edges. To understand those heterogeneous and complex data structures, clustering nodes into homogeneous groups as well as rendering a comprehensible visualisation of the data is mandatory. To address both issues, we introduce Deep-LPTM, a model-based clustering strategy relying on a variational graph auto-encoder approach as well as a probabilistic model to characterise the topics of discussion. Deep-LPTM allows to build a joint representation of the nodes and of the edges in two embeddings spaces. The parameters are inferred using a variational inference algorithm. We also introduce IC2L, a model selection criterion specifically designed to choose models with relevant clustering and visualisation properties. An extensive benchmark study on synthetic data is provided. In particular, we find that Deep-LPTM better recovers the partitions of the nodes than the state-of-the art ETSBM and STBM. Eventually, the emails of the Enron company are analysed and visualisations of the results are presented, with meaningful highlights of the graph structure.