  Graphs have been widely adopted to denote structural connections between
entities. The relations are in many cases heterogeneous, but entangled together
and denoted merely as a single edge between a pair of nodes. For example, in a
social network graph, users in different latent relationships like friends and
colleagues, are usually connected via a bare edge that conceals such intrinsic
connections. In this paper, we introduce a novel graph convolutional network
(GCN), termed as factorizable graph convolutional network(FactorGCN), that
explicitly disentangles such intertwined relations encoded in a graph.
FactorGCN takes a simple graph as input, and disentangles it into several
factorized graphs, each of which represents a latent and disentangled relation
among nodes. The features of the nodes are then aggregated separately in each
factorized latent space to produce disentangled features, which further leads
to better performances for downstream tasks. We evaluate the proposed FactorGCN
both qualitatively and quantitatively on the synthetic and real-world datasets,
and demonstrate that it yields truly encouraging results in terms of both
disentangling and feature aggregation. Code is publicly available at
https://github.com/ihollywhy/FactorGCN.PyTorch.
