ABSTRACT

The detection of community structures is a crucial research area. The problem of community detection has received considerable attention from a large portion of the scientific community and a very large number of papers has already been published in the literature. Even more important is the fact that, this large number of articles is in fact spread across a large number of different disciplines, from computer science, to statistics, and social sciences. These facts necessitate some type of classification and organization of these works. In this work, our basic classification approach divides the community detection schemes into three basic approaches: (a) the bottom-up approaches, (b) the top-down approaches, and (c) the data structure-based approaches. The first category includes the majority of algorithms, so further classification is possible. Such a classification is included in this work. For the other two categories, we make no further categorizations but we simply focus our discussion on the metrics or the data structures being used. Finally, a few possible directions for future research are also suggested.

摘要

社区结构的检测是至关重要的研究领域。社区检测的问题已经引起了很大一部分科学界的广泛关注，并且大量论文已经在文献中发表。更重要的是，事实上，如此大量的文章分布在从计算机科学到统计学和社会科学的众多不同学科中。这些事实需要对这些作品进行某种类型的分类和组织。在这项工作中，我们的基本分类方法将社区检测方案分为三个基本方法：（a）自下而上的方法，（b）自上而下的方法，以及（c）基于数据结构的方法。第一类包括大多数算法，因此可以进行进一步分类。这种分类包括在这项工作中。对于其他两个类别，我们不作进一步的分类，而只是将讨论重点放在所使用的度量或数据结构上。最后，还提出了一些未来研究的可能方向。