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Given the ubiquitous existence of graph-structured data, learning therepresentations of nodes for the downstream tasks ranging from nodeclassification, link prediction to graph classification is of crucialimportance. Regarding missing link inference of diverse networks, we revisitthe link prediction techniques and identify the importance of both thestructural and attribute information. However, the available techniques eitherheavily count on the network topology which is spurious in practice or cannotintegrate graph topology and features properly. To bridge the gap, we propose abicomponent structural and attribute learning framework (BSAL) that is designedto adaptively leverage information from topology and feature spaces.Specifically, BSAL constructs a semantic topology via the node attributes andthen gets the embeddings regarding the semantic view, which provides a flexibleand easy-to-implement solution to adaptively incorporate the informationcarried by the node attributes. Then the semantic embedding together withtopology embedding is fused together using an attention mechanism for the finalprediction. Extensive experiments show the superior performance of our proposaland it significantly outperforms baselines on diverse research benchmarks.

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