论文标题
一种令人惊讶的网络社区结构确定算法
An algorithm for network community structure determination by surprise
论文作者
论文摘要
可以从其基本的社区结构中研究代表现实世界系统的图。网络中的社区是一个直观的想法，在其客观数学定义上尚无共识。为了检测社区的最常用的指标是模块化，尽管文献中已经注意到了此参数的许多缺点。在这项工作中，我们提出了一种基于不同指标的新方法：惊喜。此外，对不同社区检测算法和基准网络的偏见进行了详尽的研究，识别和评论。
Graphs representing real world systems may be studied from their underlying community structure. A community in a network is an intuitive idea for which there is no consensus on its objective mathematical definition. The most used metric in order to detect communities is the modularity, though many disadvantages of this parameter have already been noticed in the literature. In this work, we present a new approach based on a different metric: the surprise. Moreover, the biases of different community detection algorithms and benchmark networks are thoroughly studied, identified and commented about.
