Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings

TL;DR

This paper introduces SGAAE, a novel explainable graph autoencoder that captures polarization and community structure in signed networks using archetypal analysis and GNNs, with strong empirical results.

Contribution

The paper presents SGAAE, a new framework for explainable, polarization-aware network embeddings tailored for signed graphs, addressing a gap in existing models.

Findings

Successfully infers node memberships over latent structures

Effectively characterizes network polarization and opposing communities

Outperforms baseline models in signed link prediction tasks

Abstract

Autoencoders based on Graph Neural Networks (GNNs) have garnered significant attention in recent years for their ability to extract informative latent representations, characterizing the structure of complex topologies, such as graphs. Despite the prevalence of Graph Autoencoders, there has been limited focus on developing and evaluating explainable neural-based graph generative models specifically designed for signed networks. To address this gap, we propose the Signed Graph Archetypal Autoencoder (SGAAE) framework. SGAAE extracts node-level representations that express node memberships over distinct extreme profiles, referred to as archetypes, within the network. This is achieved by projecting the graph onto a learned polytope, which governs its polarization. The framework employs a recently proposed likelihood for analyzing signed networks based on the Skellam distribution, combined with relational archetypal analysis and GNNs. Our experimental evaluation demonstrates the SGAAEs' capability to successfully infer node memberships over the different underlying latent structures while extracting competing communities formed through the participation of the opposing views in the network. Additionally, we introduce the 2-level network polarization problem and show how SGAAE is able to characterize such a setting. The proposed model achieves high performance in different tasks of signed link prediction across four real-world datasets, outperforming several baseline models.