Utilizing Cellular Learning Automata for Finding Communities in Weighted Networks

Abstract

The tremendous increase in Web usage led to the appearance of different network structures. One of the essential issues in the field of network science and engineering is to find and utilize network structures such as community structures by community detection. Although most of the current algorithms for detection of community use on the binary representation of the networks, some networks can encode more information instead of the topological structure, in which this information can be applied appropriately in detecting communities. Network information can be represented in the form of weights and identified as the weighted social network. This paper proposes a new algorithm using irregular CLA (cellular learning automaton) for finding the community in weighted networks called CLA-WCD. The CLA-WCD can find near-optimal community structures with reasonable running-time by taking advantage of the parallel capability and learning ability of the cellular automata and learning automaton, respectively. The CLA-WCD is also evaluated on real and synthetic networks in comparison with popular community discovery methods. The simulation results demonstrated that the CLA-WCD outperforms other methods.