中国物理B ›› 2025, Vol. 34 ›› Issue (12): 128901-128901.doi: 10.1088/1674-1056/adea9a

• • 上一篇    

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)§   

  1. School of Statistics and Data Science, AAIS, LPMC and KLMDASR, Nankai University, Tianjin 300071, China
  • 收稿日期:2025-04-03 修回日期:2025-06-23 接受日期:2025-07-02 发布日期:2025-11-25
  • 通讯作者: Qiqi Wang, Huijia Li E-mail:qiqi.wang@nankai.edu.cn;hjli@nankai.edu.cn
  • 基金资助:
    This work is supported by the Natural Science Foundation of China (Grant No. 72571150).

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)§   

  1. School of Statistics and Data Science, AAIS, LPMC and KLMDASR, Nankai University, Tianjin 300071, China
  • Received:2025-04-03 Revised:2025-06-23 Accepted:2025-07-02 Published:2025-11-25
  • Contact: Qiqi Wang, Huijia Li E-mail:qiqi.wang@nankai.edu.cn;hjli@nankai.edu.cn
  • About author:2025-128901-250577.pdf
  • Supported by:
    This work is supported by the Natural Science Foundation of China (Grant No. 72571150).

赞

摘要： 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.

关键词: attributed graphs, overlapping communities, neutrosophic C-means, density peaks

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.

Key words: attributed graphs, overlapping communities, neutrosophic C-means, density peaks

中图分类号:  (Structures and organization in complex systems)

  • 89.75.Fb
89.75.-k (Complex systems)