Hypergraph Neural Networks for Social Network Dynamics

Abstract

This paper introduces a novel approach to modeling social network dynamics using Hypergraph Neural Networks (HNNs). Traditional Graph Neural Networks (GNNs) struggle to accurately represent and propagate information through networks exhibiting higher-order relationships, a common characteristic of social structures. HNNs overcome this limitation by explicitly constructing hypergraphs, which represent sets of vertices connected by multiple edges. This allows for the direct modeling of complex interactions and dependencies beyond the pairwise connections considered in standard GNNs. We propose a framework for designing and training HNNs specifically tailored for social network analysis, focusing on predicting behavioral patterns and social influence. The core contribution lies in leveraging hypergraph structures to capture and propagate information across multiple connected nodes simultaneously, leading to improved accuracy in modeling dynamic social systems. The proposed method is demonstrated through a theoretical analysis and conceptual framework, highlighting the potential for future research and applications.