Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models
This provides an efficient solution for researchers analyzing large-scale network data, though it is incremental as it builds on existing stochastic block model inference methods.
The paper tackles the problem of inferring stochastic block models in large networks by introducing an algorithm that can operate as an optimized Markov chain Monte Carlo method with improved mixing and reduced trapping, or as a greedy heuristic with almost linear O(N ln^2 N) complexity. The result shows that the heuristic delivers results indistinguishable from the exact MCMC method in many networks while being much faster.
We present an efficient algorithm for the inference of stochastic block models in large networks. The algorithm can be used as an optimized Markov chain Monte Carlo (MCMC) method, with a fast mixing time and a much reduced susceptibility to getting trapped in metastable states, or as a greedy agglomerative heuristic, with an almost linear $O(N\ln^2N)$ complexity, where $N$ is the number of nodes in the network, independent on the number of blocks being inferred. We show that the heuristic is capable of delivering results which are indistinguishable from the more exact and numerically expensive MCMC method in many artificial and empirical networks, despite being much faster. The method is entirely unbiased towards any specific mixing pattern, and in particular it does not favor assortative community structures.