  Disentangled Graph Convolutional Network (DisenGCN) is an encouraging
framework to disentangle the latent factors arising in a real-world graph.
However, it relies on disentangling information heavily from a local range
(i.e., a node and its 1-hop neighbors), while the local information in many
cases can be uneven and incomplete, hindering the interpretabiliy power and
model performance of DisenGCN. In this paper, we introduce a novel Local and
Global Disentangled Graph Convolutional Network (LGD-GCN) to capture both local
and global information for graph disentanglement. LGD-GCN performs a
statistical mixture modeling to derive a factor-aware latent continuous space,
and then constructs different structures w.r.t. different factors from the
revealed space. In this way, the global factor-specific information can be
efficiently and selectively encoded via a message passing along these built
structures, strengthening the intra-factor consistency. We also propose a novel
diversity promoting regularizer employed with the latent space modeling, to
encourage inter-factor diversity. Evaluations of the proposed LGD-GCN on the
synthetic and real-world datasets show a better interpretability and improved
performance in node classification over the existing competitive models.
