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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. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a new learning algorithm is proposed for the problem of simultaneous learning of a function and its derivatives as an extension of the study of error minimized extreme learning machine for single hidden layer feedforward neural networks. Our formulation leads to solving a system of linear equations and its solution is obtained by Moore–Penrose generalized pseudo-inverse. In this approach the number of hidden nodes is automatically determined by repeatedly adding new hidden nodes to the network either one by one or group by group and updating the output weights incrementally in an efficient manner until the network output error is less than the given expected learning accuracy. For the verification of the efficiency of the proposed method a number of interesting examples are considered and the results obtained with the proposed method are compared with that of other two popular methods. It is observed that the proposed method is fast and produces similar or better generalization performance on the test data. [Copyright &y& Elsevier]