A Preferential Latent Space Model for Text Networks

TL;DR

This paper introduces a novel latent space model for text networks that integrates textual content and network structure, enabling better understanding of interactions through topic-aware embeddings and node preferences.

Contribution

It proposes a flexible, identifiable latent space model incorporating text embeddings and node-topic preferences, with a new estimation algorithm and theoretical analysis.

Findings

Model effectively captures node-topic preferences influencing edge formation.

Simulation results demonstrate accurate recovery of network and text structure.

Application to email data shows improved understanding of communication patterns.

Abstract

Network data enriched with textual information, referred to as text networks, arise in a wide range of applications, including email communications, scientific collaborations, and legal contracts. In such settings, both the structure of interactions (i.e., who connects with whom) and their content (i.e., what is communicated) are useful for understanding network relations. Traditional network analyses often focus only on the structure of the network and discard the rich textual information, resulting in an incomplete or inaccurate view of interactions. In this paper, we introduce a new modeling approach that incorporates texts into the analysis of networks using topic-aware text embedding, representing the text network as a generalized multi-layer network where each layer corresponds to a topic extracted from the data. We develop a new and flexible latent space network model that captures how node-topic preferences directly modulate edge formation, and establish identifiability conditions for the proposed model. We tackle model estimation with a projected gradient descent algorithm, and further discuss its theoretical properties. The efficacy of our proposed method is demonstrated through simulations and an analysis of an email network.