基于局部准则的多线程多尺度快速社区发现算法
Fast Multi-Scale Community Detection based on Local Criteria within a Multi-Threaded Algorithm
摘要：
许多系统可以用图或网络来描述。检测这些网络中的社区可以提供有关原始系统的底层结构和功能的信息。然而，这种检测是一项复杂的任务，在过去的十年中，大量的工作都致力于此。一个重要的特征是，社区可以在几个尺度或分辨率级别上找到，表明组织的几个级别。因此，社区结构的解决方案可能不是唯一的。此外，网络往往很大，因此需要有效的处理。在这项工作中，我们提出了一个新的算法，使用本地标准跨尺度的社区的快速检测。我们利用本地方面的标准，使并行计算，进一步提高算法的效率。该算法在大规模生成的多尺度网络上进行了测试，实验证明了该算法的有效性和准确性。
英文摘要：
Many systems can be described using graphs, or networks. Detecting communities in these networks can provide information about the underlying structure and functioning of the original systems. Yet this detection is a complex task and a large amount of work was dedicated to it in the past decade. One important feature is that communities can be found at several scales, or levels of resolution, indicating several levels of organisations. Therefore solutions to the community structure may not be unique. Also networks tend to be large and hence require efficient processing. In this work, we present a new algorithm for the fast detection of communities across scales using a local criterion. We exploit the local aspect of the criterion to enable parallel computation and improve the algorithm's efficiency further. The algorithm is tested against large generated multi-scale networks and experiments demonstrate its efficiency and accuracy.
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