Unveiling community structures in static networks through graph variational Bayes with evolution information

With the rapid development of graph variational Bayes theory, some representative community detection methods have been proposed. Although these methods are well designed, they are limited by the inherent constraints of static networks. Therefore, how to re-examine the information in static networks...

Full description

Saved in:
Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 576; p. 127349
Main Authors: Cheng, Junwei, He, Chaobo, Han, Kunlin, Chen, Gangbin, Liang, Wanying, Tang, Yong
Format: Journal Article
Language:English
Published: Elsevier B.V 01.04.2024
Subjects:
Community detection
Evolution information
K-core decomposition
Mixture of Gaussian distributions
Variational graph autoencoder
Community detection
K-core decomposition
Variational graph autoencoder
Evolution information
Mixture of Gaussian distributions
ISSN:0925-2312, 1872-8286
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the rapid development of graph variational Bayes theory, some representative community detection methods have been proposed. Although these methods are well designed, they are limited by the inherent constraints of static networks. Therefore, how to re-examine the information in static networks from a new perspective becomes a challenge. To this end, in this paper, we propose EG-VGAE and its variant EGC-VGAE, going beyond the constraints of static networks from a new perspective. Specifically, we first demonstrate that the evolution relations on static networks can be simulated reasonably. And then, our EG-VGAE method combines evolution information with static network information to realize a fine-grained propagation of local low-frequency signals to global low-frequency signals, thereby improving the accuracy of community assignment for each node. Building on this progress, our method EGC-VGAE imposes the smoothness constraint on adjacent slices, significantly enhancing the sensitivity of the method to evolution information and mitigating the impact of network noise. The comprehensive experimental results on real static networks well validate that our methods outperform state-of-the-art methods in most cases. The code is available at https://github.com/GDM-SCNU/EG-VGAE.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2024.127349