Community structure identification is an important area of research in complex social networks. Uncovering hidden communities in social networks data can help us to visualize and analyze various behavioral and structural phenomenon's occurring in social networks. Detecting communities in networks implies identification of set of clusters that show strong internal cohesion than external cohesion. The problem can be translated into modularity maximization problem which is NP hard. This paper attempts to maximize the modularity of a given network through Mod-GSO, a modification of the Group Search Optimization (GSO) algorithm based on evolutionary animal searching behavior. Mod-GSO modifies the area copying mechanism of the scrounger animals in GSO, by performing a single point crossover instead of real coded GA's crossover to evolve communities. The proposed modification is done to make GSO applicable to Social networks data and evolve scroungers with better community structures and convergence as compared to random evolution of GSO scroungers. Mod-GSO does not require the number of communities to be fixed a-priori and works with a smaller population. Experimental results obtained by Mod-GSO were compared with three well known community detection algorithms named CNM, RB, Multilevel and two evolutionary community detection algorithms named Firefly and GA-Mod using Modularity and NMI metrics on four real world and eleven well known benchmark datasets. The best modularity values of Mod-GSO were observed to be higher than many of the community finding algorithms. The results shows the capability of Mod-GSO for detecting accurate community structures in comparison to other algorithms.
['Hema Banati', 'Nidhi Arora']
Modeling Evolutionary Group Search Optimization Approach for Community Detection in Social Networks