Section for Cognitive Systems, Technical University of Denmark, 2800 Lyngby, Denmark.
Neural Comput. 2012 Sep;24(9):2434-56. doi: 10.1162/NECO_a_00314. Epub 2012 Apr 17.
Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled.
许多具有科学意义的网络自然地分解为具有相对较少外部链接的簇或社区；然而，当前网络社区的贝叶斯模型并没有发挥出这种社区的直观概念。我们为社区检测制定了一个与社区直观定义一致的非参数贝叶斯模型，并提出了一种用于推断社区结构的马尔可夫链蒙特卡罗程序。一个带有所提出的推理过程的 Matlab 工具箱可用于下载。在合成和真实网络上，我们的模型检测到与真实情况一致的社区，并且在真实网络上，它在预测缺失链接方面优于现有方法。这表明社区结构是网络的一个重要结构属性，应该明确地对其进行建模。
