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Graph-based clustering plays an important role in the clustering area. Recentstudies about graph convolution neural networks have achieved impressivesuccess on graph type data. However, in general clustering tasks, the graphstructure of data does not exist such that the strategy to construct a graph iscrucial for performance. Therefore, how to extend graph convolution networksinto general clustering tasks is an attractive problem. In this paper, wepropose a graph auto-encoder for general data clustering, which constructs thegraph adaptively according to the generative perspective of graphs. Theadaptive process is designed to induce the model to exploit the high-levelinformation behind data and utilize the non-Euclidean structure sufficiently.We further design a novel mechanism with rigorous analysis to avoid thecollapse caused by the adaptive construction. Via combining the generativemodel for network embedding and graph-based clustering, a graph auto-encoderwith a novel decoder is developed such that it performs well in weighted graphused scenarios. Extensive experiments prove the superiority of our model.

Adaptive Graph Auto-Encoder for General Data Clustering