BSAL: A Framework of Bi-component Structure and Attribute Learning for
Link Prediction
- URL: http://arxiv.org/abs/2204.09508v1
- Date: Mon, 18 Apr 2022 03:12:13 GMT
- Title: BSAL: A Framework of Bi-component Structure and Attribute Learning for
Link Prediction
- Authors: Bisheng Li, Min Zhou, Shengzhong Zhang, Menglin Yang, Defu Lian,
Zengfeng Huang
- Abstract summary: We propose a bicomponent structural and attribute learning framework (BSAL) that is designed to adaptively leverage information from topology and feature spaces.
BSAL constructs a semantic topology via the node attributes and then gets the embeddings regarding the semantic view.
It provides a flexible and easy-to-implement solution to adaptively incorporate the information carried by the node attributes.
- Score: 33.488229191263564
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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