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:基于图形的集群在组群领域起着重要作用。 最近关于图形共变神经网络的研究在图形类型数据上取得了令人印象深刻的成功。 但是,在一般组群任务中,数据图表结构并不存在,因此,构建图形的战略对于性能来说至关重要。因此,如何将图形共变网络扩展为一般组群任务是一个有吸引力的问题。在本文中,我们提议为一般数据组群建立一个图形自动编码器,该组群根据图形的遗传视角,以适应的方式构建图形。适应过程旨在引导模型利用数据背后的高层次信息,并充分利用非欧洲域域图结构。我们进一步设计了一个具有严格分析的新机制,以避免因适应性构建而导致的崩溃。Via将网络嵌入和基于图形的集群的基因化模型结合起来,一个图形自动编码器与新的解码器一起开发,使其在加权图形使用的假设情景中运行良好。广泛的实验证明了我们模型的优越性。