A Local Community Detection Method Using Expansion of Influential Nodes Set

doi:10.7652/xjtuxb201604007

您当前的位置：

首页 >

文章列表页 >

A Local Community Detection Method Using Expansion of Influential Nodes Set

更新时间：2025-07-09

- A Local Community Detection Method Using Expansion of Influential Nodes Set
- Vol. 50, Issue 4, Pages: 41-47(2016)
- 作者机构：
  
  国家数字交换系统工程技术研究中心,郑州,450002
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201604007
  CLC： TP393
- Online First：10 April 2016，
  
  Published：2016
- 稿件说明：
移动端阅览
A Local Community Detection Method Using Expansion of Influential Nodes Set[J]. 2016, 50(4): 41-47.
DOI：

A Local Community Detection Method Using Expansion of Influential Nodes Set[J]. 2016, 50(4): 41-47. DOI： 10.7652/xjtuxb201604007.

摘要

针对规模化网络中局部社团检测存在的对初始节点位置敏感、拓扑信息难以有效利用问题

提出了一种采用影响力节点集扩展的社团检测(IN-LCD)方法。首先定义了节点的局部影响力指标

通过该指标计算并构造了源节点附近的影响力节点子集

然后从影响力节点子集开始

以迭代更新的方式

进行连续的社团扩张

最后通过节点和社团相似性指标计算

完成整个局部社团的获取。IN-LCD方法从有效利用节点局部信息出发

通过最具影响力节点集合进行社团扩展

有效克服了局部社团检测对初始节点位置敏感的问题。在真实和人工网络数据集上的实验表明

IN-LCD方法与已有的最佳局部社团检测方法相比

识别性能提升了5.3%

更能有效应用于局部信息出发的社团检测场景。

Abstract

A local community detection algorithm based on influential nodes set(IN-LCD)is proposed to focus the problems that the local community detection in large-scale network is sensitive to the position of source nodes and the topology information is difficult to effectively use. A local influence index for nodes is defined

and a subset of influential nodes near the source node is calculated and constructed with the index. Then

the continuous expansion of the community is realized from the subset

and the whole local community is constructed through the calculation of the similarity index between nodes and community. The method uses the most influential nodes set to expand the community and effectively overcomes the sensitive problem of local community detection to initial node position. Experiments on real and artificial network data sets and a comparison with an existing local community detection method show that the recognition performance of the proposed IN-LCD is improved by 5.3%.

关键词

Keywords

references

GIRVAN M, NEWMAN M E. Community structure in social and biological networks [J]. Proceedings of the National Academy of Sciences of the United States of America, 2001, 99(12): 7821-7826.

杨建伟, 桂小林, 安健, 等. 一种信任关系网络中的社团结构检测算法 [J]. 西安交通大学学报, 2014, 48(12): 80-86.

YANG Jianwei, GUI Xiaolin, AN Jian, et al. Community structure detecting in trust relationship networks [J]. Journal of Xi'an Jiaotong University, 2014, 48(12): 80-86.

FORTUNATO S. Community detection in graphs [J]. Physics Reports, 2009, 486(3/4/5): 75-174.

NEWMAN M E. Fast algorithm for detecting community structure in networks [J]. Physical Review: E Statistical Nonlinear Soft Matter Physics, 2004, 69(6): 066133.

陈国强, 王宇平. 采用离散粒子群算法的复杂网络重叠社团检测 [J]. 西安交通大学学报, 2013, 47(1): 107-113.

CHEN Guoqiang, WANG Yuping. Overlapping community detection of complex networks based on discrete particle swarm algorithm [J]. Journal of Xi'an Jiaotong University, 2013, 47(1): 107-113.

卫红权, 陈鸿昶, 刘力雄, 等. 基于强度排序的通信社区检测算法 [J]. 通信学报, 2014, 35(10): 165-170.

WEI Hongquan, CHEN Hongchang, LIU Lixiong, et al. Communication community detection algorithm based on ranking of strength [J]. Journal on Communications, 2014, 35(10): 165-170.

COSCIA M, ROSSETTI G, GIANNOTTI F, et al. DEMON: a local-first discovery method for overlapping communities [C]∥Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2012: 615-623.

AARON C, NEWMAN M E J, CRISTOPHER M. Finding community structure in very large networks [J]. Physical Review: E Statistical Nonlinear Soft Matter Physics, 2004, 70(6): 264-277.

BAGROW J P, BOLLT E M. Local method for detecting communities [J]. Physical Review: E Statistical Nonlinear Soft Matter Physics, 2005, 72(4): 046108.

AARON C. Finding local community structure in networks [J]. Physical Review: E Statistical Nonlinear Soft Matter Physics, 2005, 72(2): 254-271.

LUO F, WANG J Z, PROMISLOW E. Exploring local community structures in large networks [C]∥Proceedings of 2006 IEEE International Conference on Web Intelligence. Piscataway, NJ, USA: IEEE, 2006: 233-239.

LANCICHINETTI A, FORTUNATO S, KERTESZ J. Detecting the overlapping and hierarchical community structure in complex networks [J]. New Journal of Physics, 2009, 11(15): 19-44.

USHA NANDINI R, REKA A. Near linear time algorithm to detect community structures in large-scale networks [J]. Physical Review: E Statistical Nonlinear Soft Matter Physics, 2007, 76(3): 233-239.

CHEN Q, WU T T, FANG M. Detecting local community structures in complex networks based on local degree central nodes [J]. Physica: A Statistical Mechanics Its Applications, 2013, 392(3): 529-537.

MENG F, ZHU M, ZHOU Y, et al. Local community detection in complex networks based on maximum cliques extension [J]. Mathematical Problems in Engineering, 2014, 2014(1): 1-12.

CHEN D, LV L, SHANG M S, et al. Identifying influential nodes in complex networks [J]. Fuel Energy Abstracts, 2012, 391(4): 1777-1787.

FREEMAN L C. Centrality in social networks conceptual clarification [J]. Social Networks, 1978, 1(3): 215-239.

FREEMAN L C. A set of measures of centrality based on betweenness [J]. Sociometry, 1977, 40(1): 35-41.

BONACICH P. Factoring and weighting approaches to status scores and clique identification [J]. Journal of Mathematical Sociology, 1972, 2(1): 113-120.

ANDREA L, SANTO F, FILIPPO R. Benchmark graphs for testing community detection algorithms [J]. Physical Review: E Statistical Nonlinear Soft Matter Physics, 2008, 78(4): 561-570.

ZACHARY W W. An information flow model for conflict and fission in small groups [J]. Journal of Anthropological Research, 1977, 33(4): 473-480.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Overlapping Community Detection of Complex Networks Based on Discrete Particle Swarm Algorithm

Mechanism and Optimal Control Strategy for Hysteresis in Rapid Dehumidification Response of Indirect Thermal Systems in Electric Vehicles

Electrothermal Coupling Research on the Evolutionary Law of Anti-Corona Performance for High-Altitude Air-Cooled Generators

Review of the Application of EBSILON Software in Combined Heat and Power System Optimization

Bi-Level Multi-Obj ective Optimization of a Wind-Solar Coupled Hydrogen Production System Based on the Synergy of Hybrid Electrolyzer Arrays

Related Author

陈国强 1

王宇平 1

Related Institution

School of Computer Science and Technology, Xidian University

School of Computer and Information Engineering, Henan University

⁰