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.
A real-world graph has a complex topological structure, which is often formed by the interaction of different latent factors. Disentanglement of these latent factors can effectively improve the robustness and expressiveness of node representation of graph. However, most existing methods lack consideration of the intrinsic differences in relations between nodes caused by factor entanglement. In this paper, we propose an Adversarial Disentangled Graph Convolutional Network (ADGCN) for disentangled graph representation learning. Specifically, a component-specific aggregation approach is proposed to achieve micro-disentanglement by inferring latent components that caused 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.
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topology
topological
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interpretability
expressiveness
D
the
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links
relations between nodes
R
dynamic multi-component convolution layer is designed
component-specific aggregation approach is proposed
A
the
R
that improves the separability between
to improve the separability among
A
the
R
learn collaboratively a better disentangled representation and topological
reveal the topological graph
R
diversity preserving node sampling-based progressive refinement of graph structure is proposed
diversity-preserving node sampling approach is proposed, by which the graph structure can be progressively refined in a way of local structure awareness
D
remarkably