  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.
