Interpretable Node Representation with Attribute Decoding
This work addresses interpretability in graph representation learning, though it appears incremental as it builds on existing VGAE frameworks.
The authors tackled the problem of learning interpretable node representations from graph data by proposing NORAD, a model that incorporates attribute decoding and community structure capture, which improved representation quality for isolated nodes.
Variational Graph Autoencoders (VGAEs) are powerful models for unsupervised learning of node representations from graph data. In this work, we systematically analyze 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, improving the quality of these nodes' representations. Our empirical results demonstrate the advantage of the proposed model when learning graph data in an interpretable approach.