BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction

TL;DR

This paper introduces BSAL, a novel framework that adaptively combines structural and attribute information for improved link prediction in graph-structured data, addressing limitations of existing methods.

Contribution

The paper proposes a bicomponent learning framework that effectively integrates topology and attribute information using semantic topology and attention-based fusion.

Findings

BSAL outperforms baseline methods on multiple benchmarks.

Semantic topology construction improves attribute utilization.

Attention mechanism enhances link prediction accuracy.

Abstract

Given the ubiquitous existence of graph-structured data, learning the representations of nodes for the downstream tasks ranging from node classification, link prediction to graph classification is of crucial importance. Regarding missing link inference of diverse networks, we revisit the link prediction techniques and identify the importance of both the structural and attribute information. However, the available techniques either heavily count on the network topology which is spurious in practice or cannot integrate graph topology and features properly. To bridge the gap, we propose a bicomponent structural and attribute learning framework (BSAL) that is designed to adaptively leverage information from topology and feature spaces. Specifically, BSAL constructs a semantic topology via the node attributes and then gets the embeddings regarding the semantic view, which provides a flexible and easy-to-implement solution to adaptively incorporate the information carried by the node attributes. Then the semantic embedding together with topology embedding is fused together using an attention mechanism for the final prediction. Extensive experiments show the superior performance of our proposal and it significantly outperforms baselines on diverse research benchmarks.