Trio-based collaborative multi-view graph clustering with multiple constraints

doi:10.1016/j.ipm.2020.102466

Trio-based collaborative multi-view graph clustering with multiple constraints

2021-05-01

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DOI：10.1016/j.ipm.2020.102466

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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.

Keywords:

Multi-view graph clustering

Collaborative learning

Unsupervised learning

Graph auto-encoder

Trio-based collaborative multi-view graph clustering with multiple constraints

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