Community detection from biological and social networks

Display Omitted We propose six metaheuristic optimization algorithms to solve the community detection (CD) problem.The proposed algorithms have been modified in order to use for solving modularity optimization problem which is a discrete optimization problem.The four algorithms (HDSA, BADE, SSGA and BB-BC) have been supported by new techniques or hybrid methods in addition to their original versions.Comparative analyses of the proposed algorithms are performed on the four biological and five social networks.According to acquired experimental results, it has been observed that HDSA is more efficient and competitive than the other algorithms. In order to analyze complex networks to find significant communities, several methods have been proposed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big Bang-Big Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new methods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were conducted using nine real-world complex networks - five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested.