  A real-world graph has a complex topological structure, which is often formed
by the interaction of different latent factors. However, most existing methods
lack consideration of the intrinsic differences in relations between nodes
caused by factor entanglement. In this paper, we propose an
\underline{\textbf{A}}dversarial \underline{\textbf{D}}isentangled
\underline{\textbf{G}}raph \underline{\textbf{C}}onvolutional
\underline{\textbf{N}}etwork (ADGCN) for disentangled graph representation
learning. To begin with, we point out two aspects of graph disentanglement that
need to be considered, i.e., micro-disentanglement and macro-disentanglement.
For them, a component-specific aggregation approach is proposed to achieve
micro-disentanglement by inferring latent components that cause the links
between nodes. On the basis of micro-disentanglement, we further propose a
macro-disentanglement adversarial regularizer to improve the separability among
component distributions, thus restricting the interdependence among components.
Additionally, to reveal the topological graph structure, a diversity-preserving
node sampling approach is proposed, by which the graph structure can be
progressively refined in a way of local structure awareness. The experimental
results on various real-world graph data verify that our ADGCN obtains more
favorable performance over currently available alternatives. The source codes
of ADGCN are available at \textit{\url{https://github.com/SsGood/ADGCN}}.
