签名社交网络中一种快速本地社区检测算法

Sahar Bakhtar, Hovhannes A. Harutyunyan
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引用次数: 0

摘要

近年来,随着社交网络服务的快速发展,这一领域的研究问题也越来越多。社区检测是社交网络中的一个重要问题。一个好的社区可以被定义为一组高度连接和松散连接到组外顶点的顶点。社区检测包括探索社交网络中的社区划分。考虑到社交网络是巨大的,拥有关于整个网络的完整信息几乎是不可能的。因此,近年来,当地社区检测问题变得更加普遍。这个问题可以定义为使用本地信息检测给定节点的社区。许多网络既有积极的关系,也有消极的关系。在签名网络中,社区被定义为由社区内的正链接和社区间的负链接紧密相连的一组节点。本文针对签名网络中的局部社区检测问题,提出了一种新的快速算法,命名为$Alg_{SP}$,用于识别签名网络中给定节点的密集社区。实验结果表明,该算法可以独立于起始节点检测出真实群落。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Fast Local Community Detection Algorithm in Signed Social Networks
Recent years have witnessed the rapid growth of social network services and consequently, research problems investigated in this area. Community detection is one of the most important problems in social networks. A good community can be defined as a group of vertices that are highly connected and loosely connected to the vertices outside the group. Community detection includes exploring the community partitioning in social networks. Regarding the fact that social networks are huge, having complete information about the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. This problem can be defined as the detection of a community for a given node by using local information. Many networks contain both positive and negative relations. A community in signed networks is defined as a group of nodes that are densely connected by positive links within the community and negative links between communities. In this paper, considering the problem of local community detection in signed networks, a new fast algorithm, noted as $Alg_{SP}$, is developed to identify a dense community for a given node in signed networks. Experimental results show that the proposed algorithm can detect the ground-truth communities independently from the starting nodes.
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