.png){kind=icon}
返回
Deep attributed graph clustering with self-separation regularization and parameter-free cluster estimation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2021.07.012.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Detecting clusters over attributed graphs is a fundamental task in the graph analysis field. The goal is to partition nodes into dense clusters based on both their attributes and structures. Modern graph neural networks provide facilitation to jointly capture the above information in attributed graphs with a feature aggregation manner, and have achieved great success in attributed graph clustering. However, existing methods mainly focus on capturing the proximity information in graphs and often fail to learn cluster-friendly features during the training of models. Besides, similar to many deep clustering frameworks, current methods based on graph neural networks require a preassigned cluster number before estimating the clusters. To address these limitations, we propose in this paper a deep attributed clustering method based on self-separated graph neural networks and parameter-free cluster estimation. First, to learn cluster-friendly features, we jointly optimize a jumping graph convolutional auto-encoder with a self-separation regularizer, which learns clusters with changing sizes while keeping dense intra-cluster structures and sparse inter structures. Second, an additional softmax auto-encoder is trained to determine the natural cluster number from the data. The hidden units capture cluster structures and can be used to estimate the number of clusters. Extensive experiments show the effectiveness of the proposed model. (C) 2021 Elsevier Ltd. All rights reserved.
Keyword:
Attributed graph clustering
Graph convolutional networks
Parameter-free cluster estimation
Self-separation regularization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.3
论文数:
8.2K
被引数:
3.0W
机构
引用论文
Survey: Functional Module Detection from Protein-Protein Interaction Networks调查: 蛋白质-蛋白质相互作用网络的功能模块检测
PREAI
分享收藏Networks of bZIP Protein-Protein Interactions Diversified Over a Billion Years of Evolution.png)
Science
OAAI
ScienceIF0
OAAI分享收藏 没有更多内容
没有更多内容