  A real-world graph has a complex topology structure, which is often formed by
the interaction of different latent factors. Disentanglement of these latent
factors can effectively improve the robustness and interpretability of node
representation of the graph. However, most existing methods lack consideration
of the intrinsic differences in links caused by factor entanglement. In this
paper, we propose an Adversarial Disentangled Graph Convolutional Network
(ADGCN) for disentangled graph representation learning. Specifically, a dynamic
multi-component convolution layer is designed to achieve micro-disentanglement
by inferring latent components that caused links between nodes. On the basis of
micro-disentanglement, we further propose a macro-disentanglement adversarial
regularizer that improves the separability between component distributions,
thus restricting interdependence among components. Additionally, to learn
collaboratively a better disentangled representation and topological structure,
a diversity preserving node sampling-based progressive refinement of graph
structure is proposed. The experimental results on various real-world graph
data verify that our ADGCN obtains remarkably more favorable performance over
currently available alternatives.
