Priyanka Gupta and Dr. Shikha Gupta
Adv. Artif. Intell. Mach. Learn., 3 (4):1728-1742
Priyanka Gupta : Shaheed Sukhdev College of Business Studies. University of Delhi
Dr. Shikha Gupta : Associate Professor(Computer Science), Shaheed Sukhdev College of Business Studies, University of Delhi
DOI: https://dx.doi.org/10.54364/AAIML.2023.1199
Article History: Received on: 03-Oct-23, Accepted on: 18-Dec-23, Published on: 25-Dec-23
Corresponding Author: Priyanka Gupta
Email: priyanka.cs.du@gmail.com
Citation: Dr. Shikha Gupta and Priyanka Gupta (2023). Parameter Tuning of Coronavirus Herd Immunity Optimizer for Detection of Communities in Social Networks. Adv. Artif. Intell. Mach. Learn., 3 (4 ):1728-1742
Community detection is an NP-Hard problem that concerns
itself with partitioning a network into groups such that nodes within a
partition enjoy denser network connections compared to nodes in different
partitions. The capacity to locate and examine these groups can offer
invaluable assistance in comprehending and illustrating the framework of
networks. Since the community detection problem is inherently complex,
metaheuristic optimization algorithms are extensively employed to address this
problem. A recently proposed metaheuristic population-based algorithm, Coronavirus
Herd Immunity Optimizer (CHIO), draws inspiration from the COVID-19 herd
immunity treatment strategy. We adapt the CHIO algorithm for the problem of
community detection in social networks. In this proposal, the Network Modularity
value is computed to assess the quality of a community structure.
Tuning the parameters of a metaheuristic algorithm to a given problem at
hand is essential for good algorithm performance and is the focus of our
proposal. The parameter tuning method developed by Genichi Taguchi is utilized
to fine-tune the parameters of the CHIO algorithm in the context of community
detection. Experiments on real-world benchmark networks were conducted. The
adapted CHIO algorithm is run with parameter values obtained after tuning. It
is noticed that the proposed approach is successful in detecting community
structures with a high modularity value.