  Disentangled representation learning has recently attracted a significant
amount of attention, particularly in the field of image representation
learning. However, learning the disentangled representations behind a graph
remains largely unexplored, especially for the attributed graph with both node
and edge features. Disentanglement learning for graph generation has
substantial new challenges including 1) the lack of graph deconvolution
operations to jointly decode node and edge attributes; and 2) the difficulty in
enforcing the disentanglement among latent factors that respectively influence:
i) only nodes, ii) only edges, and iii) joint patterns between them. To address
these challenges, we propose a new disentanglement enhancement framework for
deep generative models for attributed graphs. In particular, a novel
variational objective is proposed to disentangle the above three types of
latent factors, with novel architecture for node and edge deconvolutions.
Moreover, within each type, individual-factor-wise disentanglement is further
enhanced, which is shown to be a generalization of the existing framework for
images. Qualitative and quantitative experiments on both synthetic and
real-world datasets demonstrate the effectiveness of the proposed model and its
extensions.
