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}}.

翻译：真实世界图具有复杂的拓扑结构，这种结构通常由不同潜在因素的相互作用形成。然而，现有方法大多未考虑因因素纠缠导致的节点间关系的本质差异。本文提出一种对抗性解耦图卷积网络（ADGCN）用于解耦图表示学习。首先指出图解耦需考虑的两个方面——微观解耦与宏观解耦。针对前者，提出组件特异性聚合方法，通过推断导致节点间连接的潜在组件实现微观解耦。在微观解耦基础上，进一步提出宏观解耦对抗正则化器以提升组件分布间的可分离性，从而限制组件间的相互依赖关系。此外，为揭示拓扑图结构，提出多样性保持节点采样方法，通过局部结构感知方式逐步优化图结构。在多种真实图数据上的实验结果表明，ADGCN相比现有方法具有更优性能。ADGCN源代码已开源至：\textit{\url{https://github.com/SsGood/ADGCN}}。