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.

翻译：基于图的聚类在聚类领域中扮演着重要角色。近年来，关于图卷积神经网络的研究在图数据类型上取得了显著成功。然而，在通用聚类任务中，数据的图结构并不存在，因此图构建策略对性能至关重要。如何将图卷积网络扩展到通用聚类任务中是一个引人关注的问题。本文提出了一种面向通用数据聚类的图自编码器，该编码器根据图的生成视角自适应地构建图。自适应过程旨在引导模型挖掘数据背后的高层信息，并充分利用非欧几里得结构。我们进一步设计了一种基于严格分析的新机制，以避免自适应构建导致的坍塌问题。通过结合网络嵌入的生成模型与基于图的聚类，我们开发了一种带有新型解码器的图自编码器，使其在加权图场景中表现优异。大量实验证明了本模型的优越性。