Open Access

ARTICLE

Neighbor Dual-Consistency Constrained Attribute-Graph Clustering^#

Tian Tian^1,2, Boyue Wang^1,2, Xiaxia He^1,2,*, Wentong Wang³, Meng Wang¹

1 School of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China
2 Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing, 100124, China
3 Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China

* Corresponding Author: Xiaxia He. Email:

Computers, Materials & Continua 2025, 85(3), 4885-4898. https://doi.org/10.32604/cmc.2025.067795

Received 13 May 2025; Accepted 24 July 2025; Issue published 23 October 2025

Abstract

Attribute-graph clustering aims to divide the graph nodes into distinct clusters in an unsupervised manner, which usually encodes the node attribute feature and the corresponding graph structure into a latent feature space. However, traditional attribute-graph clustering methods often neglect the effect of neighbor information on clustering, leading to suboptimal clustering results as they fail to fully leverage the rich contextual information provided by neighboring nodes, which is crucial for capturing the intrinsic relationships between nodes and improving clustering performance. In this paper, we propose a novel Neighbor Dual-Consistency Constrained Attribute-Graph Clustering that leverages information from neighboring nodes in two significant aspects: neighbor feature consistency and neighbor distribution consistency. To enhance feature consistency among nodes and their neighbors, we introduce a neighbor contrastive loss that encourages the embeddings of nodes to be closer to those of their similar neighbors in the feature space while pushing them further apart from dissimilar neighbors. This method helps the model better capture local feature information. Furthermore, to ensure consistent cluster assignments between nodes and their neighbors, we introduce a neighbor distribution consistency module, which combines structural information from the graph with similarity of attributes to align cluster assignments between nodes and their neighbors. By integrating both local structural information and global attribute information, our approach effectively captures comprehensive patterns within the graph. Overall, our method demonstrates superior performance in capturing comprehensive patterns within the graph and achieves state-of-the-art clustering results on multiple datasets.

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Keywords

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