The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges

Abstract

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.

29 pages including the appendix, 13 figures, 6 tables, journal paper

References in corpus (10)

• Adam: A Method for Stochastic Optimization
• Efficient Estimation of Word Representations in Vector Space
• Auto-Encoding Variational Bayes
• An Introduction to Variational Autoencoders
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models
• Probabilistic Latent Semantic Analysis
• Deep Convolutional Networks on Graph-Structured Data
• Clustering with Deep Learning: Taxonomy and New Methods
• The Stochastic Topic Block Model for the Clustering of Vertices in Networks with Textual Edges
• Embedded Topics in the Stochastic Block Model