吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (1): 242-248.doi: 10.13229/j.cnki.jdxbgxb201701035

• 论文 • 上一篇    下一篇

基于密度峰值的重叠社区发现算法

时小虎1, 2, 3, 4, 冯国香1, 2, 李牧1, 2, 李瑛1, 2, 吴春国1, 2, 3, 4   

  1. 1.吉林大学 计算机科学与技术学院,长春 130012;
    2.吉林大学 符号计算与知识工程教育部重点实验室,长春 130012;
    3.吉林大学珠海学院 计算机科学与技术系,广东 珠海 519041;
    4.吉林大学珠海学院 符号计算与知识工程教育部重点实验室珠海分实验室,广东 珠海 519041
  • 收稿日期:2015-05-27 出版日期:2017-01-20 发布日期:2017-01-20
  • 通讯作者: 吴春国(1976-),男,副教授,博士.研究方向:机器学习.E-mail:wucg@jlu.edu.cn
  • 作者简介:时小虎(1974-),男,教授,博士生导师.研究方向:机器学习.E-mail:shixh@jlu.edu.cn
  • 基金资助:
    国家自然科学基金项目(61373050); 吉林省科技发展计划项目(20130101070JC, 20130522118JH, 20130206003SF); 在线教育研究基金(全通教育)项目(2017YB129); 广东省优势重点学科建设项目; 珠海市优势学科建设项目.

Overlapping community detection method based on density peaks

SHI Xiao-hu1, 2, 3, 4, FENG Guo-xiang1, 2, LI Mu1, 2, LI Ying1, 2, WU Chun-guo1, 2, 3, 4   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;
    3.Department of Computer Science and Technology, Zhuhai College of Jilin University, Zhuhai 519041, China;
    4.Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai 519041, China
  • Received:2015-05-27 Online:2017-01-20 Published:2017-01-20

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摘要： 根据基于快速搜索和发现密度峰值的聚类方法的思想,提出了基于密度峰值的重叠社区发现算法。首先定义新的距离矩阵算法,克服了邻接矩阵元素为整数的缺陷。然后用概率形式刻画每个节点属于不同类别的可能性,从而实现了重叠社区的划分。基于真实网络的实验结果验证了本文算法的可行性和有效性。

关键词: 计算机应用, 复杂网络, 社区发现, 重叠社区, 小世界特性, 模块度

Abstract: Based on the idea of clustering method by fast search and find of density peaks, we put forward an overlapping community detection method based on density peaks. In the proposed algorithm, first a new distance matrix is defined to overcome the defects of adjacency matrix being filled with integers. Then, the probability form is used to depict the possibility of each node that belongs to different clusters. The experimental results on real network show that the proposed method performs very well.

Key words: computer application, complex network, community detection, overlapping community, small world feature, modularity

中图分类号: 

  • TP391
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