计算机科学
嵌入
自编码
利用
人工智能
模块化(生物学)
群落结构
Boosting(机器学习)
机器学习
节点(物理)
数据挖掘
动态网络分析
深度学习
数学
生物
工程类
组合数学
结构工程
遗传学
计算机安全
计算机网络
作者
Tianpeng Li,Wenjun Wang,Pengfei Jiao,Yinghui Wang,Ruomeng Ding,Huaming Wu,Lin Pan,Di Jin
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-11-01
卷期号:53 (11): 7021-7033
被引量:2
标识
DOI:10.1109/tcyb.2022.3168343
摘要
Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real world. A variety of methods, such as modularity optimization, spectral method, and statistical network model, has been developed from diversified perspectives. Recently, network embedding-based technologies have made significant progress, and one can exploit deep learning superiority to network tasks. Although some methods for static networks have shown promising results in boosting community detection by integrating community embedding, they are not suitable for temporal networks and unable to capture their dynamics. Furthermore, the dynamic embedding methods only model network varying without considering community structures. Hence, in this article, we propose a novel unsupervised dynamic community detection model, which is based on network embedding and can effectively discover temporal communities and model dynamic networks. More specifically, we propose the community prior by introducing the Gaussian mixture model (GMM) in the variational autoencoder, which can obtain community information and better model the evolutionary characteristics of community structure and node embedding by utilizing the variant of gated recurrent unit (GRU). Extensive experiments conducted in real-world and artificial networks demonstrate that our proposed model has a better effect on improving the accuracy of dynamic community detection.
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