Parallel Multi-label Propagation for Overlapping Community Detection in Large-Scale Networks
Parallel Multi-label Propagation for Overlapping Community Detection in Large-Scale Networks
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DOI：
10.1007/978-3-319-26181-2_33
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Rongrong Li;Wenzhong Guo;Kun Guo;Qirong Qiu
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文献类型：
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作者：
Rongrong Li;Wenzhong Guo;Kun Guo;Qirong Qiu
近年来，随着网络规模的快速增长，现有的许多算法很难发现大规模网络中的社区。提出了一种新的并行多标签传播算法(PMLPA)来检测网络中的重叠社区。PMLPA在每次迭代的标签传播过程中使用了一种新的使用踝值的标签更新策略。由于Spark框架在分布式并行计算中的强大功能，新算法在Spark框架中得以实现。在人工网络和真实网络上的实验表明，PMLPA在大规模网络中的社区发现中是有效和高效的。
In recent years, with the rapid growth of network scale, it becomes difficult to detect communities in large-scale networks for many existing algorithms. In this paper, a novel Parallel Multi-Label Propagation Algorithm (PMLPA) is proposed to detect the overlapping communities in networks. PMLPA employs a new label updating strategy using ankle-value in the label propagation procedure during each iteration. The new algorithm is implemented in the Spark framework for its power in distributed parallel computation. Experiments on artificial and real networks show that PMLPA is effective and efficient in community detection in large-scale networks.