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Deep graph clustering, which aims to group the nodes of a graph into disjointclusters with deep neural networks, has achieved promising progress in recentyears. However, the existing methods fail to scale to the large graph withmillion nodes. To solve this problem, a scalable deep graph clustering method(Dink-Net) is proposed with the idea of dilation and shrink. Firstly, bydiscriminating nodes, whether being corrupted by augmentations, representationsare learned in a self-supervised manner. Meanwhile, the cluster centres areinitialized as learnable neural parameters. Subsequently, the clusteringdistribution is optimized by minimizing the proposed cluster dilation loss andcluster shrink loss in an adversarial manner. By these settings, we unify thetwo-step clustering, i.e., representation learning and clustering optimization,into an end-to-end framework, guiding the network to learn clustering-friendlyfeatures. Besides, Dink-Net scales well to large graphs since the designed lossfunctions adopt the mini-batch data to optimize the clustering distributioneven without performance drops. Both experimental results and theoreticalanalyses demonstrate the superiority of our method. Compared to the runner-up,Dink-Net achieves 9.62% NMI improvement on the ogbn-papers100M dataset with 111million nodes and 1.6 billion edges. The source code is released athttps://github.com/yueliu1999/Dink-Net. Besides, a collection (papers, codes,and datasets) of deep graph clustering is shared athttps://github.com/yueliu1999/Awesome-Deep-Graph-Clustering.

Dink-Net: Neural Clustering on Large Graphs