Parallel Overlapping Community Detection with SLPA
Parallel Overlapping Community Detection with SLPA
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DOI：
10.1109/socialcom.2013.37
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发表时间：
2013-09
期刊：
影响因子：
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通讯作者：
Konstantin Kuzmin;B. Szymański;Thesis Advisor;Mark K Goldberg;Sibel Adali
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文献类型：
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作者：
Konstantin Kuzmin;B. Szymański;Thesis Advisor;Mark K Goldberg;Sibel Adali
社交网络由拥有共同特征的成员的各种社区组成。一个社区的一些成员往往也是其他社区的成员。不同社区的这种共同成员身份导致社区重叠。检测这种重叠的社区是一个具有挑战性和计算密集型的问题。在本文中，我们调查的可用性高性能计算领域的社交网络和社区检测。我们提出了一个高度可扩展的变体社区检测算法称为扬声器-监听器标签传播算法（SLPA）。我们发现，尽管不规则的数据依赖的计算，并行计算的范例可以显着加快检测的重叠社区的社交网络，这是计算昂贵的。通过实验，我们展示了如何利用各种并行计算架构来分析共享内存机器和分布式内存机器（如IBM Blue Gene）上的大型社交网络数据。
Social networks consist of various communities that host members sharing common characteristics. Often some members of one community are also members of other communities. Such shared membership of different communities leads to overlapping communities. Detecting such overlapping communities is a challenging and computationally intensive problem. In this paper, we investigate the usability of high performance computing in the area of social networks and community detection. We present highly scalable variants of a community detection algorithm called Speaker-listener Label Propagation Algorithm (SLPA). We show that despite of irregular data dependencies in the computation, parallel computing paradigms can significantly speed up the detection of overlapping communities of social networks which is computationally expensive. We show by experiments, how various parallel computing architectures can be utilized to analyze large social network data on both shared memory machines and distributed memory machines, such as IBM Blue Gene.