Link Prediction Based on Subgraph Learning in Biological Networks

Abstract

Recently, link prediction (LP) based on graph neural networks (GNNs) methods has achieved notable successes in biological networks (BNs), since it can reveal the organizational principles, functional mechanisms, and dynamic properties of biological systems. However, these LPs still face some significant challenges that need to be addressed in BNs. The first is the complex and heterogeneous characteristics of BNs. Moreover, BN structures often have the dynamic addition and removal of nodes and edges over time or across physiological states. Afterward, there are asymmetries and hierarchical modularity in the structures of BNs. Finally, high computational complexity has resulted from the above challenges in BNs. Therefore, this article proposes a novel GNN-based LP model via local clustering and subgraphs, termed LCS in short, to address these issues in BNs. LCS introduces a subgraph-based GNN approach to effectively address the heterogeneous characteristics inherent in complex BNs, along with the consequent challenges of asymmetry and hierarchical modularity. Furthermore, LCS designs a dynamic local subgraph extraction (SE) mechanism based on heat kernel diffusion and the Chopper pruning algorithm. This mechanism leverages the effective local clustering properties of heat diffusion and uses Chopper to achieve linear-time SE, mitigating subgraph size explosion and enhancing LP efficiency. Additionally, by imposing diversity regularization constraints, the method reduces computational complexity and improves generalization performance. Experimental results on four BN benchmarks demonstrate that LCS achieves significant improvements over existing state-of-the-art LP methods. The implementation of LCS is publicly available at https://github.com/XL0104/LCS-Model.git.