Explainable Graph Neural Networks for Social Network Analysis

Abstract

This paper presents a novel approach to social network analysis utilizing Explainable Graph Neural Networks (E-GNNs). Traditional graph neural networks, while effective in capturing complex relationships within networks, often operate as black boxes, hindering interpretability and trust. We propose a framework that integrates the expressive power of GNNs with explainable AI techniques, specifically focusing on attention mechanisms and SHAP (SHapley Additive exPlanations) values, to provide transparent and understandable insights into network dynamics. Our methodology allows for the identification of influential nodes and the understanding of the features driving their influence. This work addresses the critical need for interpretability in GNN applications, particularly within the domain of social network analysis, ultimately enhancing the reliability and validity of network-based inferences. The core claim is the design of an E-GNN model for analyzing complex relationships and patterns within social networks. The core mechanism involves combining GNNs with explainability techniques, and the novelty lies in directly addressing the interpretability challenges of GNNs for social network applications.