Overlapping Community Detection Based on Influence and Seeds Extension

YU Zhi-yong; CHEN Ji-jie; GUO Kun; CHEN Yu-zhong; XU Qian

doi:10.3969/j.issn.0372-2112.2019.01.020

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Overlapping Community Detection Based on Influence and Seeds Extension

更新时间：2025-07-16

- Overlapping Community Detection Based on Influence and Seeds Extension
- Acta Electronica Sinica Vol. 47, Issue 1, Pages: 153-160(2019)
- 作者机构：
  
  1. 福州大学数学与计算机科学学院,福建,福州,350116
  2. 福建省网络计算与智能信息处理重点实验室,福建,福州,350116
  3. 空间数据挖掘与信息共享教育部重点实验室,福建,福州,350116
  4. 国网信通亿力科技有限责任公司,福建,福州,350003
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61300104, No.61772136, No.61672158);Outstanding Youth Science Fund of Colleges and Universities in Fujian Province (No.JA12016);New Century Excellent Talents Support Plan of Higher Education of Fujian Province (No.JA13021);Science Foundation for Outstanding Youth of Fujian Province (No.2014J06017, No.2015J06014);Science and Technology Innovation Platform of Fujian Province (No.2009J1007, No.2014H2005);Natural Science Foundation of Fujian Province (No.2013J01230, No.2014J01232);Industry-university Cooperation Program of Fujian Province (No.2014H6014, No.2017H6008);Haixi Government Big Data Application Collaborative Innovation Center
- DOI：10.3969/j.issn.0372-2112.2019.01.020
  CLC： TP393
- Published Online：25 January 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
YU Zhi-yong, CHEN Ji-jie, GUO Kun, et al. Overlapping Community Detection Based on Influence and Seeds Extension[J]. Acta Electronica Sinica, 2019, 47(1): 153-160.
DOI：

YU Zhi-yong, CHEN Ji-jie, GUO Kun, et al. Overlapping Community Detection Based on Influence and Seeds Extension[J]. Acta Electronica Sinica, 2019, 47(1): 153-160. DOI： 10.3969/j.issn.0372-2112.2019.01.020.

摘要

社区发现作为复杂社交网络中一个重要的研究方向.针对目前基于种子节点的算法在种子选取与扩展等方面的不足，提出了一种基于影响力与种子扩展的重叠社区发现算法（Influence Seeds Extension Overlapping Community Detection，简称i-SEOCD算法）.首先，利用节点影响力策略找出具有紧密结构的种子社区.其次，从这些种子社区出发，计算社区邻居集节点与社区的相似度，并取出相似度超过设定阈值的节点.然后，采用优化自适应函数的策略来扩展社区.最后，对网络中的自由节点进行社区隶属划分，进而实现了整个网络的重叠社区结构挖掘.在真实社交网络和人工生成网络上实验表明，i-SEOCD算法能够准确、快速地发现复杂网络中的重叠社区结构.

Abstract

Community detection is a significant research direction in the research of social networks.To improve the quality of seeds selection and expansion

we propose an influence seeds extension overlapping community detection (i-SEOCD) algorithm for overlapping community detection.First

i-SEOCD uses a node influence strategy to find the seed communities with tight structures.Second

on the basis of the seed communities

we calculate the similarity among communities and their neighbor nodes.The nodes whose similarity is greater than a predefined threshold are selected.Third

the strategy of optimizing a self-adaptive function is adopted to expand the communities.Finally

the free nodes in the network are assigned to their corresponding communities in order to find out all the overlapping community structures.Experiments on the real and artificial networks show that i-SEOCD is capable of discovering overlapping communities in complex social networks efficiently.

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