A cellular learning automata based algorithm for detecting community structure in complex networks

• We introduce the cellular learning automata to solve the problem of community detection in complex networks.
• Our algorithm overcomes the resolution limit of modularity optimization through the interactions with both global and local environments.
• The tests on both synthetic and real-world networks show our algorithm is effective and promising for community detection in complex networks.

Community structure is a common and important property of complex networks. The detection of communities has great significance for understanding the function and organization of networks. Generally, community detection can be formulated as a modularity optimization problem. However, traditional modularity optimization based algorithms have the resolution limit that they may fail to find communities which are smaller than a certain size. In this paper, we propose a cellular learning automata based algorithm for detecting communities in complex networks. Our algorithm models the whole network as an irregular cellular learning automata (ICLA) and reveals the optimal community structure through the evolution of the cellular learning automata. By interacting with both the global and local environments, our algorithm effectively solves the resolution limit problem of modularity optimization. The experiments on both synthetic and real-world networks demonstrate that our algorithm is effective and efficient at detecting community structure in complex networks.