压缩大型和松散的社区

V. Chandrashekar, Shailesh Kumar, C. V. Jawahar
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引用次数: 0

摘要

大型网络中紧密重叠社区的检测是一个重要的模式识别问题,在许多领域都有应用。大多数社区检测算法在社区大小、紧凑性和发现社区的可扩展性之间进行权衡。Clique peration Method (CPM)和Local Fitness Maximization (LFM)是两种重要且常用的大型网络重叠社区检测方法。然而,他们发现的大量社区规模大、嘈杂、松散。在本文中,我们提出了一种通用算法,该算法可以将任何方法生成的如此大且松散的社区以系统的方式精炼成紧凑的社区。我们定义了一种基于特征向量中心性的新的社区度量,利用该度量来识别松散社区,并提出了一种将松散社区划分为紧密社区的算法。我们使用我们的方法对CPM和LFM发现的社区进行了细化,并在推荐引擎任务中与原始社区进行了比较,展示了它们的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Compacting Large and Loose Communities
Detecting compact overlapping communities in large networks is an important pattern recognition problem with applications in many domains. Most community detection algorithms trade-off between community sizes, their compactness and the scalability of finding communities. Clique Percolation Method (CPM) and Local Fitness Maximization (LFM) are two prominent and commonly used overlapping community detection methods that scale with large networks. However, significant number of communities found by them are large, noisy, and loose. In this paper, we propose a general algorithm that takes such large and loose communities generated by any method and refines them into compact communities in a systematic fashion. We define a new measure of community-ness based on eigenvector centrality, identify loose communities using this measure and propose an algorithm for partitioning such loose communities into compact communities. We refine the communities found by CPM and LFM using our method and show their effectiveness compared to the original communities in a recommendation engine task.
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