Direct Comparative Analysis of Nature-inspired Optimization Algorithms on Community Detection Problem in Social Networks
This work provides a comparative analysis for researchers in social network analysis, but it is incremental as it applies existing methods to a known problem.
The paper tackled the community detection problem in social networks by directly comparing four nature-inspired optimization algorithms (NIOAs) on three real-world networks, measuring performance using five scores based on the prasatul matrix and average isolability.
Nature-inspired optimization Algorithms (NIOAs) are nowadays a popular choice for community detection in social networks. Community detection problem in social network is treated as optimization problem, where the objective is to either maximize the connection within the community or minimize connections between the communities. To apply NIOAs, either of the two, or both objectives are explored. Since NIOAs mostly exploit randomness in their strategies, it is necessary to analyze their performance for specific applications. In this paper, NIOAs are analyzed on the community detection problem. A direct comparison approach is followed to perform pairwise comparison of NIOAs. The performance is measured in terms of five scores designed based on prasatul matrix and also with average isolability. Three widely used real-world social networks and four NIOAs are considered for analyzing the quality of communities generated by NIOAs.