Link prediction on multi-relational graphs from an influence propagation perspective

2026-08-05

AI Summary

This work proposes Influence Propagation-based Graph Neural Network (IGNP), a novel framework for link prediction in multi-relational graphs. The approach uniquely models relational interactions between nodes as an influence diffusion process, leveraging an extended SIR epidemic model to capture global structural information within large-scale subgraphs. To enhance scalability, IGNP incorporates a virtual edge compression technique that substantially reduces computational complexity. By effectively integrating local node features with global topological patterns, the method achieves significant performance gains over strong existing baselines across multiple real-world datasets, thereby demonstrating the efficacy and superiority of modeling multi-relational link prediction through the lens of influence propagation.

Abstract

Predicting the existence and type of links (edges) between nodes in a multi-relational graph is key for applications from social interaction prediction to knowledge relationship identification. Enhancing local features with relevant global information is crucial for accurate link prediction, yet it remains challenging. We address this by modeling the relationship between node pairs as node influence. That is, whether the node influence can be propagated and what type of influence is propagated indicates where and what type the edge is, which will be the most relevant local and global information to predict the edges. To this end, we extend the Susceptible-Infectious-Recovered (SIR) epidemic model to capture the influence propagation of nodes on a large scale through sub-graph structures. Subsequently, these sub-graphs are compressed using virtual edges, thereby substantially reducing the computation associated with utilizing the global graph structure. Finally, we propose the Influential Graph Neural Predictor, referred to as IGNP, a link prediction framework guided by influence propagation. Extensive experiments demonstrate the superiority of the proposed method, which outperforms strong baselines by a large margin on the widely used and real-world datasets.

Problem

• link prediction
• multi-relational graphs
• influence propagation
• global information
• node influence

Innovation

• influence propagation
• multi-relational graph
• link prediction
• SIR model
• graph neural networks