| INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
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Overlapping community detection on attributed graphs via neutrosophic C-means |
| Yuhan Jia(贾雨涵)†, Leyan Ouyang(欧阳乐严)†, Qiqi Wang(王萁淇)‡, and Huijia Li(李慧嘉)§ |
| School of Statistics and Data Science, AAIS, LPMC and KLMDASR, Nankai University, Tianjin 300071, China |
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Abstract Detecting overlapping communities in attributed networks remains a significant challenge due to the complexity of jointly modeling topological structure and node attributes, the unknown number of communities, and the need to capture nodes with multiple memberships. To address these issues, we propose a novel framework named density peaks clustering with neutrosophic C-means. First, we construct a consensus embedding by aligning structure-based and attribute-based representations using spectral decomposition and canonical correlation analysis. Then, an improved density peaks algorithm automatically estimates the number of communities and selects initial cluster centers based on a newly designed cluster strength metric. Finally, a neutrosophic C-means algorithm refines the community assignments, modeling uncertainty and overlap explicitly. Experimental results on synthetic and real-world networks demonstrate that the proposed method achieves superior performance in terms of detection accuracy, stability, and its ability to identify overlapping structures.
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Received: 03 April 2025
Revised: 23 June 2025
Accepted manuscript online: 02 July 2025
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PACS:
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89.75.Fb
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(Structures and organization in complex systems)
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89.75.-k
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(Complex systems)
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| Fund: This work is supported by the Natural Science Foundation of China (Grant No. 72571150). |
Corresponding Authors:
Qiqi Wang, Huijia Li
E-mail: qiqi.wang@nankai.edu.cn;hjli@nankai.edu.cn
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| About author: 2025-128901-250577.pdf |
Cite this article:
Yuhan Jia(贾雨涵), Leyan Ouyang(欧阳乐严), Qiqi Wang(王萁淇), and Huijia Li(李慧嘉) Overlapping community detection on attributed graphs via neutrosophic C-means 2025 Chin. Phys. B 34 128901
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