论文标题
深层嵌入聚类，并保存分布一致性的属性网络
Deep Embedded Clustering with Distribution Consistency Preservation for Attributed Networks
论文作者
论文摘要
现实世界中的许多复杂系统都可以通过归因网络来表征。为了挖掘这些网络中的潜在信息，近年来，嵌入式群集同时获得了节点表示和群集。在不同视图中数据的一致性的假设下，网络拓扑的群集结构和节点属性的群集对于属性网络应该是一致的。但是，许多现有方法忽略了此属性，即使它们分别从网络拓扑和节点属性中编码节点表示形式，同时在表示向量上从其中一个视图中学到的群集节点。因此，在这项研究中，我们为归因网络提出了一个端到端的深嵌入聚类模型。它利用Graph AutoCododer和Node属性自动编码器分别学习节点表示和群集分配。此外，引入了分布一致性约束，以维持两个视图的群集分布的潜在一致性。在几个数据集上进行的广泛实验表明，与最先进的方法相比，所提出的模型可以更好或竞争性能。可以在https://github.com/zhengymm/dcp上找到源代码。
Many complex systems in the real world can be characterized by attributed networks. To mine the potential information in these networks, deep embedded clustering, which obtains node representations and clusters simultaneously, has been paid much attention in recent years. Under the assumption of consistency for data in different views, the cluster structure of network topology and that of node attributes should be consistent for an attributed network. However, many existing methods ignore this property, even though they separately encode node representations from network topology and node attributes meanwhile clustering nodes on representation vectors learnt from one of the views. Therefore, in this study, we propose an end-to-end deep embedded clustering model for attributed networks. It utilizes graph autoencoder and node attribute autoencoder to respectively learn node representations and cluster assignments. In addition, a distribution consistency constraint is introduced to maintain the latent consistency of cluster distributions of two views. Extensive experiments on several datasets demonstrate that the proposed model achieves significantly better or competitive performance compared with the state-of-the-art methods. The source code can be found at https://github.com/Zhengymm/DCP.
