Abstract
Social network analysis (SNA) is one of the emerging fields of research for discovering the densely connected nodes in social networks (community detection) and identifying the behavioral patterns of the communities. Changes in the behavioral pattern of the communities are called Events. The application of the similarity finding technique on the detected communities that facilitates the detection of events is called community mining. Event detection depends on a minimum permissible level of similarity value (adopted for the set of nodes in a community), called the threshold value of similarity. The traditional approaches randomly selected this threshold value of similarity causing unequal distribution of community events. This paper uses evolutionary algorithms to tune the threshold value (k) of similarity for equal distribution of community events in a massive dataset . This paper evaluates three different fitness functions (Root-Mean-Squared Error, Pairwise Sum of Squared Differences, and Entropy) with an ultimate goal to achieve uniformity in the detection of events. The experimental results confirm that maximizing the Entropy (proved using the Lagrangian multiplier method) is the best strategy among the three fitness functions in order to get a uniform distribution of events. This paper compares the performance of Cuckoo Search Algorithm (CSA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ASO) for tuning the similarity threshold value using Entropy-based fitness function. The experimental results show that CSA facilitates a uniform classification of events as compared to PSO and ACO. The empirical analysis validates the similarity threshold value of 0.48 for classifying all the events uniformly. It is observed that CSA is approximately 54.95% and 71.08% faster than PSO and ACO, respectively.
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Rajita, B.S.A.S., Bansal, M., Narwa, B.S. et al. Cuckoo search in threshold optimization for better event detection in social networks. Soc. Netw. Anal. Min. 12, 38 (2022). https://doi.org/10.1007/s13278-022-00867-y
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DOI: https://doi.org/10.1007/s13278-022-00867-y