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Towards a Heuristic Algorithm for Partitioning Network Community

机译:面向网络社区划分的启发式算法

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摘要

Community detection is one of the key problems in the field of complex network analysis. In the paper, we mainly focus on the two-part division problem for network, i.e. community (or graph) partitioning. Based on the in-depth analysis on the partitioning results, a two-stage heuristic algorithm named SPC is proposed. It firstly identifies two pseudo-centers, and then generates two semi-communities by removing some undecided nodes. In the next step, it adopts an experience rule to classify such nodes. The experiment results show that the SPC algorithm is effective and can yield the best partitioning results for most instances.
机译:社区检测是复杂网络分析领域的关键问题之一。在本文中,我们主要关注网络的两部分划分问题,即社区(或图)划分。在对划分结果进行深入分析的基础上,提出了一种两阶段启发式算法SPC。它首先标识两个伪中心,然后通过删除一些未确定的节点来生成两个半社区。在下一步中,它将采用经验规则对此类节点进行分类。实验结果表明,SPC算法是有效的,并且可以在大多数情况下产生最佳的分区结果。

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