相关社区的快速多尺度检测
Fast Multi-Scale Detection of Relevant Communities
摘要：
如今，网络几乎无处不在。在过去的十年中，社区发现作为一种通过将节点分组为内部连接比外部连接更紧密的社区来揭示网络结构的方法受到越来越多的关注。然而，现有的大多数有效方法都没有考虑网络可能包含的潜在组织水平或规模，因此是有限的。在本文中，我们提出了一种方法兼容的全球和当地的标准，使快速多尺度社区发现。该方法是在两个算法，每种类型的标准之一，并与6个已知的标准。发现不同规模的社区是一项计算成本很高的任务。因此，这项工作把重点放在减少计算复杂性。为了加快速度，引入了一些算法。实验证明了我们的方法的效率和准确性，相对于每个算法和标准，通过测试它们对大型生成的多尺度网络。这项研究还提供了一个标准之间的比较和全球和地方的方法。
英文摘要：
Nowadays, networks are almost ubiquitous. In the past decade, community detection received an increasing interest as a way to uncover the structure of networks by grouping nodes into communities more densely connected internally than externally. Yet most of the effective methods available do not consider the potential levels of organisation, or scales, a network may encompass and are therefore limited. In this paper we present a method compatible with global and local criteria that enables fast multi-scale community detection. The method is derived in two algorithms, one for each type of criterion, and implemented with 6 known criteria. Uncovering communities at various scales is a computationally expensive task. Therefore this work puts a strong emphasis on the reduction of computational complexity. Some heuristics are introduced for speed-up purposes. Experiments demonstrate the efficiency and accuracy of our method with respect to each algorithm and criterion by testing them against large generated multi-scale networks. This study also offers a comparison between criteria and between the global and local approaches.
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