A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks

TL;DR

This paper introduces a novel hybrid model for network embedding that captures community structures and signed interactions, demonstrating improved link prediction and interpretability on real-world networks.

Contribution

The paper proposes the Hybrid Membership-Latent Distance Model (HM-LDM) and its signed extension (sHM-LDM), integrating latent space constraints and signed network likelihoods for enhanced network analysis.

Findings

Successfully identifies distinct network structures.

Achieves superior link prediction performance.

Provides interpretable visualizations of node memberships.

Abstract

Graph representation learning (GRL) has become a prominent tool for furthering the understanding of complex networks providing tools for network embedding, link prediction, and node classification. In this paper, we propose the Hybrid Membership-Latent Distance Model (HM-LDM) by exploring how a Latent Distance Model (LDM) can be constrained to a latent simplex. By controlling the edge lengths of the corners of the simplex, the volume of the latent space can be systematically controlled. Thereby communities are revealed as the space becomes more constrained, with hard memberships being recovered as the simplex volume goes to zero. We further explore a recent likelihood formulation for signed networks utilizing the Skellam distribution to account for signed weighted networks and extend the HM-LDM to the signed Hybrid Membership-Latent Distance Model (sHM-LDM). Importantly, the induced likelihood function explicitly attracts nodes with positive links and deters nodes from having negative interactions. We demonstrate the utility of HM-LDM and sHM-LDM on several real networks. We find that the procedures successfully identify prominent distinct structures, as well as how nodes relate to the extracted aspects providing favorable performances in terms of link prediction when compared to prominent baselines. Furthermore, the learned soft memberships enable easily interpretable network visualizations highlighting distinct patterns.