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首页> 外文会议>IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining >Discovering Communities of Community Discovery
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Discovering Communities of Community Discovery

机译：发现社区发现社区

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摘要

Discovering communities in complex networks means grouping nodes similar to each other, to uncover latent information about them. There are hundreds of different algorithms to solve the community detection task, each with its own understanding and definition of what a “community” is. Dozens of review works attempt to order such a diverse landscape - classifying community discovery algorithms by the process they employ to detect communities, by their explicitly stated definition of community, or by their performance on a standardized task. In this paper, we classify community discovery algorithms according to a fourth criterion: the similarity of their results. We create an Algorithm Similarity Network (ASN), whose nodes are the community detection approaches, connected if they return similar groupings. We then perform community detection on this network, grouping algorithms that consistently return the same partitions or overlapping coverage over a span of more than one thousand synthetic and real world networks. This paper is an attempt to create a similarity-based classification of community detection algorithms based on empirical data. It improves over the state of the art by comparing more than seventy approaches, discovering that the ASN contains well-separated groups, making it a sensible tool for practitioners, aiding their choice of algorithms fitting their analytic needs.
机译：在复杂网络中发现社区意味着对彼此类似的节点进行分组，以揭示关于它们的潜在信息。有数百种不同的算法来解决社区检测任务，每个算法都有自己的理解和定义“社区”是什么。几十次审查工作试图通过他们专门的社区定义或在标准化任务上的表现来检测社区来检测社区的过程，或者通过他们在标准化任务上的表现来命令这种多样化的景观 - 分类社区发现算法。在本文中，我们根据第四个标准对社区发现算法进行分类：结果的相似之处。我们创建一个算法相似度网络（ASN），其节点是社区检测方法，如果它们返回类似的分组。然后，我们对该网络进行社区检测，分组算法始终如一地返回超过一千个合成和现实世界网络的跨度相同的分区或重叠覆盖。本文基于经验数据，试图创建基于相似性的社区检测算法的分类。它通过比较超过七十个方法来改善本领域的状态，发现ASN包含分离良好的组，使其成为从业者的合理工具，帮助他们选择适合其分析需求的算法。

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