Adaptive Graph Auto-Encoder for General Data Clustering
This addresses a domain-specific problem for clustering researchers by providing an incremental improvement in graph-based methods for general data.
The paper tackles the problem of extending graph convolutional networks to general clustering tasks where graph structures do not exist, by proposing an adaptive graph auto-encoder that constructs graphs based on a generative perspective and avoids collapse, achieving superior performance in experiments.
Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. However, in general clustering tasks, the graph structure of data does not exist such that the strategy to construct a graph is crucial for performance. Therefore, how to extend graph convolution networks into general clustering tasks is an attractive problem. In this paper, we propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs. The adaptive process is designed to induce the model to exploit the high-level information behind data and utilize the non-Euclidean structure sufficiently. We further design a novel mechanism with rigorous analysis to avoid the collapse caused by the adaptive construction. Via combining the generative model for network embedding and graph-based clustering, a graph auto-encoder with a novel decoder is developed such that it performs well in weighted graph used scenarios. Extensive experiments prove the superiority of our model.