一种有效的社交网络社区检测算法

Yuan Huang, Wei Hou, Xiaowei Li, Shaomei Li
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引用次数: 3

摘要

传统的社交网络社区检测算法普遍缺乏对链接属性的考虑,充分利用链接属性信息的模型和机制来表达。针对这一问题,本文提出了融合链接属性和节点属性的社交网络社区检测算法。结合相邻节点间节点属性的相似度和链路信息,定义相似权值。在此基础上,结合凝聚算法实现对社交网络的社区检测。实验表明,该算法对社会网络属性的识别效果显著,在属性不同的社区中效果明显。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An effective community detection algorithm of the social networks
The traditional social network community detection algorithms generally lack of consideration of link attributes, and full expression using link attribute information model and mechanism. Aiming at this issue, this paper puts forward the community detection algorithm of social network through fusion the link and node attributes. We combine similarity of node attributes between adjacent nodes and link information, and define the similar weights. On this basis, the algorithm realizes community detection of the social network by combining condensation algorithm. Experiments show that effect of this algorithm about social network attribute is remarkable, obviously in attribute-distinct community.
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