Real-positive-neighbors guide Contrastive Graph Clustering Network

Real-positive-neighbors guide Contrastive Graph Clustering Network | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Expert Systems This is a preprint and has not been peer reviewed. Data may be preliminary. 6 June 2025 V1 Latest version Share on Real-positive-neighbors guide Contrastive Graph Clustering Network Authors : Jing Yang , Chulei Xiang , Wenjun Xu , Zihao Zhao , Jinrui Zhang , and Huaming Wu 0009-0001-4094-1783 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174920052.21417704/v1 328 views 209 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The rapid advancement of deep learning has introduced promising techniques for attribute graph clustering. However, existing deep attributed graph clustering methods face two key limitations: (1) insufficient exploration of multi-scale neighborhood structural information during training, and (2) inappropriate graph data augmentation strategies, which often lead to semantic drift and indistinguishable positive samples. To address these issues, this paper proposes a novel Real-positive-neighbors Guided Contrastive Graph Clustering Network (ReCogNet) for attribute graph clustering. ReCogNet employs a dynamic attention-weighted fusion mechanism to refine shallow semantic information derived from the multi-scale GCN network, enabling the model to capture subtle yet critical node relationships. Additionally, it dynamically identifies real-positive-neighbor nodes and adopts a negative-free contrastive learning objective. This objective maximizes the similarity between a query node and its real-positive-neighbors in the latent embedding space, thereby improving clustering performance by leveraging meaningful local relationships. Extensive experiments on six benchmark datasets demonstrate that the proposed ReCogNet method consistently outperforms state-of-the-art approaches. Supplementary Material File (manuscript.pdf) Download 9.40 MB Information & Authors Information Version history V1 Version 1 06 June 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Expert Systems Keywords attributed graph clustering contrastive learning data clustering deep clustering graph neural network Authors Affiliations Jing Yang Anhui Wenda University of Information Engineering View all articles by this author Chulei Xiang Tongling University View all articles by this author Wenjun Xu Huainan Normal University View all articles by this author Zihao Zhao Anhui Agricultural University View all articles by this author Jinrui Zhang Hefei University of Technology View all articles by this author Huaming Wu 0009-0001-4094-1783 [email protected] Anhui University of Finance and Economics View all articles by this author Metrics & Citations Metrics Article Usage 328 views 209 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jing Yang, Chulei Xiang, Wenjun Xu, et al. Real-positive-neighbors guide Contrastive Graph Clustering Network. Authorea . 06 June 2025. 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