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Inspired by the generation power of generative adversarial networks (GANs) inimage domains, we introduce a novel hierarchical architecture for learningcharacteristic topological features from a single arbitrary input graph viaGANs. The hierarchical architecture consisting of multiple GANs preserves bothlocal and global topological features and automatically partitions the inputgraph into representative stages for feature learning. The stages facilitatereconstruction and can be used as indicators of the importance of theassociated topological structures. Experiments show that our method producessubgraphs retaining a wide range of topological features, even in earlyreconstruction stages (unlike a single GAN, which cannot easily identify suchfeatures, let alone reconstruct the original graph). This paper is firstlineresearch on combining the use of GANs and graph topological analysis.

Learning Graph Topological Features via GAN