HCNA: Hyperbolic Contrastive Learning Framework for Self-Supervised Network Alignment

Abstract

Network alignment, or identifying the same entities (anchors) across multiple networks, has significant applications across diverse fields. Unsupervised approaches for network alignment, though popular, strictly assume that the anchor nodes’ structure and attributes remain consistent across different networks. However, in practice, strictly adhering to these constraints makes it difficult to deal with networks with high variance in the structural characteristics and inherent structural noises like missing nodes and edges, resulting in poor generalization. In order to handle these shortcomings, we propose HCNA: Hyperbolic Contrastive Learning Framework for Self-Supervised Network Alignment, a novel self-supervised contrastive learning model which learns from the multiple augmented views of each network, thereby making HCNA robust to the inherent multi-network characteristics. Furthermore, we propose multi-order hyperbolic graph convolution networks to generate node embedding for each network which can handle the hierarchical structure of networks. The main objective of HCNA is to obtain structure-preserving embeddings that are also robust to noises and variations for better alignment results. The major novelty lies in generating augmented multiple graph views for contrastive learning that are driven by real world network dynamics. Rigorous investigations on 4 real datasets show that HCNA consistently outperforms the baselines by at least 1–84% in terms of accuracy score. Furthermore, HCNA is also more resilient to structural and attributes noises, as evidenced by its adaptivity analysis on adversarial conditions.