采用影响力节点集扩展的局部社团检测

常振超; 陈鸿昶; 黄瑞阳; 于洪涛; 刘阳

doi:10.7652/xjtuxb201604007

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采用影响力节点集扩展的局部社团检测

更新时间：2025-07-09

- 采用影响力节点集扩展的局部社团检测
- A Local Community Detection Method Using Expansion of Influential Nodes Set
- 西安交通大学学报 2016年50卷第4期页码：41-47
- 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：
  
  : 2015-10-25。作者简介: 常振超(1987—),男,博士生;陈鸿昶(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61171108);国家重点基础研究发展计划资助项目(2012CB315901);国家科技支撑计划资助项目(2014BAH30B01)
- DOI：10.7652/xjtuxb201604007
  中图分类号： TP393
- 网络首发：2016-04-10，
  
  纸质出版：2016
- 稿件说明：
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常振超, 陈鸿昶, 黄瑞阳, 等. 采用影响力节点集扩展的局部社团检测[J]. 西安交通大学学报, 2016,50(4):41-47.

A Local Community Detection Method Using Expansion of Influential Nodes Set[J]. 2016, 50(4): 41-47.
常振超, 陈鸿昶, 黄瑞阳, 等. 采用影响力节点集扩展的局部社团检测[J]. 西安交通大学学报, 2016,50(4):41-47. DOI： 10.7652/xjtuxb201604007.

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%.

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Keywords

references

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