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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

With the advancement of representation learning, graph representation learning has gained significant attention in the field of community detection for heterogeneous networks. A prominent approach in this domain involves the use of meta-paths to capture higher-order relationships between nodes, particularly when bidirectional or reciprocal relationships exist. However, defining effective meta-paths often requires substantial domain expertise. Moreover, these methods typically depend on additional clustering algorithms, which can limit their practical applicability. To address these challenges, context paths have been introduced as an alternative to meta-paths. When combined with a self-attention mechanism, models can dynamically assess the relative importance of different context paths. By leveraging the inherent symmetry within context paths, models enhance their ability to capture balanced relationships between nodes, thereby improving their representation of complex interactions. Building on this idea, we propose BP-GCN, a self-attention-based model for heterogeneous community detection. BP-GCN autonomously identifies node relationships within symmetric context paths, significantly improving community detection accuracy. Furthermore, the model integrates the Bernoulli–Poisson framework to establish an end-to-end detection system that eliminates the need for auxiliary clustering algorithms. Extensive experiments on multiple real-world datasets demonstrate that BP-GCN consistently outperforms existing benchmark methods.

Details

Title
The Heterogeneous Network Community Detection Model Based on Self-Attention
Author
Zhou, Gaofeng 1   VIAFID ORCID Logo  ; Rui-Feng, Wang 2   VIAFID ORCID Logo 

 College of Computer Science & Technology, Qingdao University, Qingdao 266071, China 
 College of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China; [email protected]; National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China 
First page
432
Publication year
2025
Publication date
2025
Publisher
MDPI AG
e-ISSN
20738994
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3181702276
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.