PSO与LPA相结合用于重叠群落的检测

Qi Zhang, Mingfeng Ge, Jiao Fu
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引用次数: 2

摘要

随着社区检测的发展,人们提出了许多用于重叠社区检测的算法。这些算法可能存在一些不稳定因素,从而带来一些不理想的结果。为了提高算法的性能,提出了一种基于粒子群优化(PSO)算法和标签传播算法(LPA)的PSO_LPA算法。PSO_LPA算法以PSO算法为框架,寻找不重叠的社团结构。PSO_LPA算法基于得到的不重叠社团结构,对每个节点进行标记,标记描述节点属于社团的概率。在标签的帮助下,PSO_LPA算法可以通过一些加权运算找到重叠节点。该算法在多个真实网络上进行了测试。实验表明,与现有算法相比,新算法效率更高,准确率更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PSO combined with LPA for the detection of overlapping community
Since community detection become a hot subject, lots of algorithms for overlapping community detection have been proposed. These algorithms may have some unstable factors which would bring some undesirable results. In order to improve the performance of the algorithm, a novel algorithm, PSO_LPA, based both on particle swarm optimization (PSO) algorithm and label propagation algorithm (LPA), is proposed. The PSO_LPA algorithm uses PSO algorithm as the framework to find the non-overlapping community structure. Based on the obtained non-overlapping community structure, the PSO_LPA algorithm marks each node with a label which describes the probability of the node belongs to the communities. With the help of the labels, the PSO_LPA algorithm can find the overlapping nodes by some weighted operations. The proposed algorithm is tested on several real-world networks. According to the experiment, the new algorithm, compared to the existing algorithms, is more efficient and accurate.
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