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MREGDN: Multi-Relation Enhanced Graph Disentangled Network for semi-supervised node classification
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DOI：10.1016/j.eswa.2024.123973.png)
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Abstract
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Many deep graph neural networks perform well in tackling semi -supervised node classification tasks. However, they often focus on learning a holistic representation for nodes, which lacks interpretability and compactness for classification purposes. In this paper, we propose a Multi -Relation Enhanced Graph Disentangled Network (MREGDN), which can effectively disentangle the latent factors that affect graph topology, while enhancing the interpretability and low -redundancy of the learned node representations for classification. There are three types of relation constraints designed elaborately to optimize the disentangled representation learning process: (a) constraints of intra-factor compactness to encourage a factor to represent a distribution, (b) constraints of inter -factor irrelevance to ensure diversity among factors, and (c) constraints of keeping inter -node similarity in different spaces to retain necessary information related to category. Extensive experiments verify that MREGDN significantly outperforms its best-known competitors on various semi -supervised node classification tasks.
Keywords:
Semi-supervised learning
Node classification
Factor compactness
Factor irrelevance
Information redundancy
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