度量网络社区结构质量的并行工具箱

Mingming Chen, Sisi Liu, B. Szymanski
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引用次数: 6

摘要

许多网络显示出社区结构,这种结构可以识别节点组,其中节点组内的连接比节点组之间的连接更密集。这种社区结构的检测和表征称为社区检测,是网络系统研究的基本问题之一。它在过去几年中受到了相当大的关注。已经开发了许多高效和有效的社区检测技术。其中,效率最高的算法是标签传播算法,其计算复杂度为0 (|E|)。虽然它在边数上是线性的,但对于非常大的网络来说,运行时间仍然太长,这就需要并行社区检测。此外,计算社区结构的社区质量度量在有或没有基础真值的情况下都是计算昂贵的。然而,到目前为止,我们还没有意识到为这个问题引入并行性的任何努力。在本文中,我们提供了一个并行工具包来计算这些度量的值。我们分别在分布式内存机和共享内存机上对并行算法进行了评估。实验结果表明,在总运行时间、加速和效率方面,它们比顺序执行产生了显著的性能增益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallel Toolkit for Measuring the Quality of Network Community Structure
Many networks display community structure which identifies groups of nodes within which connections are denser than between them. Detecting and characterizing such community structure, which is known as community detection, is one of the fundamental issues in the study of network systems. It has received a considerable attention in the last years. Numerous techniques have been developed for both efficient and effective community detection. Among them, the most efficient algorithm is the label propagation algorithm whose computational complexity is O (|E|). Although it is linear in the number of edges, the running time is still too long for very large networks, creating the need for parallel community detection. Also, computing community quality metrics for community structure is computationally expensive both with and without ground truth. However, to date we are not aware of any effort to introduce parallelism for this problem. In this paper, we provide a parallel toolkit to calculate the values of such metrics. We evaluate the parallel algorithms on both distributed memory machine and shared memory machine. The experimental results show that they yield a significant performance gain over sequential execution in terms of total running time, speedup, and efficiency.
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