Three different modified discrete versions of dynamic arithmetic optimization algorithm for detection of cohesive subgroups in social networks

Abstract

Many networks in nature, society and technology are represented by the level of organization, where groups of nodes form tightly connected units called communities or modules that are only weakly connected to each other. Social networks can be thought of as a group or community, which are groups of nodes with a large number of connections to each other. Identifying these communities by modularity helps to solve the modularity maximization problem. The modularity value determines the quality of the resulting community. Community detection (CD) helps to uncover potential sub-community structures in the network that play a critical role in various research areas. Since CD problems have NP-hard problem structure, it is very difficult to obtain the optimal modularity value with classical methods. Therefore, metaheuristics are frequently preferred in the literature for solving CD problems. In this study, the DAOA algorithm, which has been recently proposed for solving continuous problems, is adapted to the CD problem. In order to improve the solution quality of the DAOA algorithm, some modifications were made in the core parameters. In addition, global and local search supports were added to the DAOA algorithm and three different modifications were applied to the algorithm in total. According to the results performed under equal conditions, among the three modified algorithms, the algorithm with parameter modification was the best in 2 out of 5 networks. DAOA with global search was the best in 3 networks, while the algorithm with local search was the best in 2 networks. However, the basic DAOA could not achieve the best result in any of the 5 networks. This clearly shows the success of the modifications on the algorithm. On the other hand, when compared with the algorithms in the literature, the proposed DAOA algorithm achieved 80% success out of 10 algorithms in total. This shows that the proposed DAOA algorithm can be used as an alternative for discrete problems.