College of Computer Science, Sichuan University of Science and Engineering, Zigong 643000, People's Republic of China.
Chaos. 2020 Feb;30(2):023114. doi: 10.1063/1.5130523.
It is a challenging problem to assign communities in a complex network so that nodes in a community are tightly connected on the basis of higher-order connectivity patterns such as motifs. In this paper, we develop an efficient algorithm that detects communities based on higher-order structures. Our algorithm can also detect communities based on a signed motif, a colored motif, a weighted motif, as well as multiple motifs. We also introduce stochastic block models on the basis of higher-order structures. Then, we test our community detection algorithm on real-world networks and computer generated graphs drawn from the stochastic block models. The results of the tests indicate that our community detection algorithm is effective to identify communities on the basis of higher-order connectivity patterns.
在复杂网络中，根据更高阶的连接模式（如模体）将社区分配给节点是一个具有挑战性的问题。在本文中，我们开发了一种基于高阶结构检测社区的有效算法。我们的算法还可以基于有符号模体、彩色模体、加权模体以及多个模体来检测社区。我们还基于高阶结构引入了随机块模型。然后，我们在真实网络和基于随机块模型生成的计算机图形上测试我们的社区检测算法。测试结果表明，我们的社区检测算法能够有效地根据高阶连接模式识别社区。
