The Maximum Community Partition Problem in Networks

Zaixin Lu, Weili Wu, Weidong Chen, Jiaofei Zhong, Yuanjun Bi, Zheng Gao
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引用次数: 10

Abstract

We proposed a community structure detection problem which aims to analyze the relationships among the data via the network topology. We collect a series of unified definitions for community structures and formulate the community structure detection into a combinatorial optimization problem to identify as many communities as possible for a given network. For some well known community definitions, we prove that there is no polynomial time optimal solution for this maximum partition problem unless P = NP, and we develop a heuristic algorithm based on greedy strategy for it. The experimental results on many real networks show that the proposed algorithm is effective in terms of the number of valid communities and the modularity score.
网络中的最大团体划分问题
我们提出了一种社区结构检测问题,旨在通过网络拓扑分析数据之间的关系。我们收集了一系列社区结构的统一定义,并将社区结构检测转化为一个组合优化问题,以在给定网络中识别尽可能多的社区。对于一些已知的群体定义,我们证明了除非P = NP,否则不存在多项式时间最优解,并提出了一种基于贪心策略的启发式算法。在多个真实网络上的实验结果表明,该算法在有效社区数量和模块化评分方面是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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