论文标题
适用于通用数据群集的自适应图自动编码器
Adaptive Graph Auto-Encoder for General Data Clustering
论文作者
论文摘要
基于图的聚类在聚类区域起着重要作用。关于图形卷积神经网络的最新研究在图类型数据上取得了令人印象深刻的成功。但是，在一般的聚类任务中，数据的图形结构不存在，因此构造图的策略对于性能至关重要。因此，如何将图形卷积网络扩展到一般聚类任务是一个有吸引力的问题。在本文中，我们为一般数据聚类提出了一个图形自动编码器，该图表根据图的生成透视图适应图。自适应过程旨在诱导模型来利用数据背后的高级信息并充分利用非欧几里得结构。我们进一步设计了一种具有严格分析的新型机制，以避免由适应性结构引起的崩溃。通过结合网络嵌入和基于图的聚类的生成模型，开发了带有新解码器的图形自动编码器，以使其在加权图的使用方案中表现良好。广泛的实验证明了我们的模型的优势。
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.
