摘要
现实世界网络的规模越来越大,使得检测社区的工作变得更具有挑战性.提出了一种可扩展的局部社区检测方法,可有效地发现网络中给定节点的重叠社区.通过考虑图中链接对的相似性及它们在多个环境中的参与程度,确定加入链接对的顺序,形成有意义的分层社区.实验评估时使用了5个大型真实网络的真实社区,结果表明,LDLC算法在准确性和效率方面都显著优于最先进的方法.
The growing size of real-world networks makes detecting community more challenging.An extensible local community detection method is proposed,which can effectively find the overlapping communities of a given node in the network.By considering the similarity of the link pairs in the figure and their participation in multiple environments,determine the order in which the link pairs are added to form a meaningful hierarchical community.The results show that the LDLC algorithm is significantly superior to the most advanced methods in both accuracy and efficiency.
作者
王一萍
WANG Yiping(School of Computer and Control Engineering,Qiqihar University,Qiqihar 161006,China)
出处
《高师理科学刊》
2020年第10期27-30,45,共5页
Journal of Science of Teachers＇College and University
关键词
复杂网络
等级社区
社区检测
分散
complex networks
hierarchical communities
community detection
dispersion
