research-article

Graph clustering network with structure embedding enhanced

Authors:

Ling DingAuthors Info & Claims

Volume 144, Issue C

https://doi.org/10.1016/j.patcog.2023.109833

Published: 01 December 2023 Publication History

Abstract

Recently, deep clustering utilizing Graph Neural Networks has shown good performance in the graph clustering. However, the structure information of graph was underused in existing deep clustering methods. Particularly, the lack of concern on mining different types structure information simultaneously. To tackle with the problem, this paper proposes a Graph Clustering Network with Structure Embedding Enhanced (GC-SEE) which extracts nodes importance-based and attributes importance-based structure information via a feature attention fusion graph convolution module and a graph attention encoder module respectively. Additionally, it captures different orders-based structure information through multi-scale feature fusion. Finally, a self-supervised learning module has been designed to integrate different types structure information and guide the updates of the GC-SEE. The comprehensive experiments on benchmark datasets commonly used demonstrate the superiority of the GC-SEE. The results showcase the effectiveness of the GC-SEE in exploiting multiple types of structure for deep clustering.

Highlights

•

The focus on structural information learned by GCN and GAE varies.

•

We propose a Graph Clustering Network with Structure Embedding Enhanced (GC-SEE).

•

GC-SEE integrates different types structure information to enrich the embedding.

•

A self-supervised loss is designed to achieve clear boundaries and high accuracy.

•

GC-SEE outperforms the methods using single structure information.

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Index Terms

Graph clustering network with structure embedding enhanced
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
    2. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Pattern Recognition

Pattern Recognition Volume 144, Issue C

Dec 2023

766 pages

ISSN:0031-3203

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Elsevier Ltd.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 December 2023

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