We propose a generative model to detect globally optimal community structures in networks by utilizing random walks. Sophisticated parameter optimization algorithms are developed based on the Markov chain Monte Carlo methods to overcome limitations of the EM algorithm, which has been used in previous works but is sometimes trapped in local optima depending on initial conditions. We apply the algorithms to synthetic and real-world networks to examine their performance in terms of precision and robustness of detected communities. It is found that the Gibbs samplers outperform the previous approaches especially in detecting overlapping communities. The Markovian dynamics of random walkers is crucial to robustly detect the optimal community structures.

中文翻译：

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用于网络社区检测的随机游走器的贝叶斯建模

我们提出了一个生成模型，通过利用随机游走来检测网络中的全局最优社区结构。复杂的参数优化算法是基于马尔可夫链蒙特卡罗方法开发的，以克服 EM 算法的局限性，该算法已在以前的工作中使用，但有时会根据初始条件陷入局部最优。我们将算法应用于合成和现实世界的网络，以检查它们在检测到的社区的精度和稳健性方面的性能。发现 Gibbs 采样器优于以前的方法，尤其是在检测重叠社区方面。随机游走者的马尔可夫动力学对于稳健地检测最佳社区结构至关重要。