[1]王冰玉,吴振宇,沈苏彬.一种社交网络的增量社区检测算法及实现优化[J].计算机技术与发展,2018,28(10):64-69.[doi:10.3969/ j. issn.1673-629X.2018.10.013]
　WANG Bing-yu,WU Zhen-yu,SHEN Su-bin.An Incremental Community Detection Algorithm for Social Networks and Its Optimization[J].,2018,28(10):64-69.[doi:10.3969/ j. issn.1673-629X.2018.10.013]
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一种社交网络的增量社区检测算法及实现优化()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年10期
页码:
64-69
栏目:
智能、算法、系统工程
出版日期:
2018-10-10

文章信息/Info

Title:
An Incremental Community Detection Algorithm for Social Networks and Its Optimization
文章编号:
1673-629X(2018)10-0064-06
作者:
王冰玉1; 吴振宇1; 沈苏彬2
1. 南京邮电大学 物联网学院,江苏 南京 210000; 2. 南京邮电大学 计算机学院,江苏 南京 210000
Author(s):
WANG Bing-yu1; WU Zhen-yu1; SHEN Su-bin2
1. School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210000,China; 2. School of Computer Science &Technology,Nanjing University of Posts and Telecommunications,Nanjing 210000,China
关键词:
社交网络; 社区检测; 增量更新
Keywords:
K Clique; social network; community detection; incremental updating
分类号:
TP391
DOI:
10.3969/ j. issn.1673-629X.2018.10.013
文献标志码:
A
摘要:
社区检测是社交网络中常用的分析手段,目的是发现网络中联系较为紧密的节点集群,提取集群,从而进一步探索集群隐含的信息。 现实中的社交网络随时间不断增大,传统的社区检测算法在不断增大的网络中运行会十分耗时,这是传统社区检测算法的一个极大弊端。 针对该问题,基于传统的 K-Clique 社区检测算法,提出一种增量 K-Clique 社区检测算法。 与传统 K-Clique 相比,增量 K-Clique 使用网络中新增的边和节点去更新已有的社区检测结果,而非在时间片更新时对整个网络重新进行社区检测,算法忽略极少部分的细节换取整体的高效性。 实验结果表明,增量社区检测算法较传统算法在执行效率上提高显著,且检测结果与传统 K-Clique 几乎吻合。
Abstract:
Community detection is a common tool to analyze social networks,aiming to discover the clusters of elements that are more closely connected in the network and then extract the clusters so as to further explore the hidden information of the cluster. As social network in real life is growing over time,using traditional community detection methods would be very time-consuming. This is a great disadvantage of the traditional community detection algorithm. In view of this,we propose an incremental K-Clique community detection algorithm based on the traditional K-Clique,which uses the edges and nodes in the new time slice to update the existing community instead of re-conducting community detection on the entire network at time slice updates. The algorithm ignores a small part of details in exchange for the overall efficiency. The experiment shows that the incremental community detection algorithm improves the execution efficiency significantly compared with the traditional one,and the detection results are almost consistent with the traditional K-clique.

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更新日期/Last Update: 2018-10-10