Detecting Community Structure of Complex Networks by Simulated Annealing with Optimal Prediction

Given a large and complex network, we would like to find the best partition of this network into a small number of clusters. This question has been addressed in many different ways. Here we utilize the simulated annealing strategy to maximize the modularity of a network with our previous hard partitioning formulation for the community structure, which is based on the optimal prediction of a random walker Markovian dynamics on the network. It is demonstrated that this simulated annealing with optimal prediction (SAOP) algorithm can efficiently and automatically determine the number of communities during the cooling procedure associated with iterative steps. Moreover, the algorithm is successfully applied to three model problems.