Interpretable Node Representation with Attribute Decoding

Variational Graph Autoencoders (VGAEs) are powerful models for unsupervised learning of node representations from graph data. In this work, we make a systematic analysis of modeling node attributes in VGAEs and show that attribute decoding is important for node representation learning. We further propose a new learning model, interpretable NOde Representation with Attribute Decoding (NORAD). The model encodes node representations in an interpretable approach: node representations capture community structures in the graph and the relationship between communities and node attributes. We further propose a rectifying procedure to refine node representations of isolated notes, which improves the quality of the representations of these nodes. Our empirical results demonstrate the advantage of the proposed model when learning graph data in an interpretable approach.

Authors

• Xiaohui Chen
• Xi Chen
• Liping Liu

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