Paper Info
Title
Trio-based collaborative multi-view graph clustering with multiple constraints
Abstract
Multi-view graph clustering is an attentional research topic in recent years due to its wide applications. According to recent surveys, most existing works focus on incorporating comprehensive information among multiple views to achieve the clustering task. However, these studies pay less attention to explore the collaborative relationship between fusion-view features and independence-view features. To make full use of view relationships and enhance the complementary benefits of different views in graphs, we propose a trio-based collaborative learning framework for multi-view graph representation clustering (TCMGC) that drives the multiple auto-clustering constraints. We utilize the triplet operations (trio-based) to guarantee the independence and complementarity between each view and complete clustering tasks collaboratively. Meanwhile, we propose a joint optimization objective to improve the overall performance of representation learning and clustering. Experimental results on four real-world benchmark datasets show that the proposed TCMGC has promising performance compared with state-of-the-art baseline methods.
Year
DOI
Venue
2021
10.1016/j.ipm.2020.102466
Information Processing & Management
Keywords
DocType
Volume
Multi-view graph clustering,Collaborative learning,Unsupervised learning,Graph auto-encoder
Journal
58
Issue
ISSN
Citations 
3
0306-4573
2
PageRank 
References 
Authors
0.40
27
6
Name
Order
Citations
PageRank
Ru Wang1338.57
Lin Li2156.32
Xiaohui Tao327038.85
Xiao Dong4288.98
Peipei Wang521.75
Pei-yu Liu61411.18