Community detection for networks based on Monte Carlo type algorithms

The community detection is a significant problem in network data analysis. In this paper, we implement community detection by minimizing an objective function based on the difference between the adjacency matrix and its expected value, and explain the rationality of the objective function. To solve the optimization problem, we propose a new algorithm which is referred to the thoughts of Markov Chain Monte Carlo and low discrepancy sequence in the random simulation fields. We introduce a new indicator to compare the performance of the methods by measuring the similarity of the true community and the estimated community. Synthetic networks and real networks are analyzed to investigate the effectiveness of the new method. Results show that the performance of the proposed method is stable in all simulated scenarios. And in most cases, it outperforms existing methods.