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Published in: New Generation Computing 2/2023

04-04-2023

Application of Evolutionary Algorithms in Social Networks: A Comparative Machine Learning Perspective

Authors: B. S. A. S. Rajita, Pranay Tarigopula, Phanindra Ramineni, Ashank Sharma, Subhrakanta Panda

Published in: New Generation Computing | Issue 2/2023

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Abstract

Social networks exhibit interactions that lead to event changes in their communities. It is imperative to track community events to understand an extensive social network. Recently, several models reported that the randomness and sparsity of social networks bring significant challenges in predicting community events. Hence, the proposed work extracts both community and temporal features to predict the events effectively that a community might experience. Machine learning (ML) models are widely used in predicting such events in a social network. Many machine learning models, such as naive Bayes, random forest, logistic regression, SVM, neural networks, etc., are used to predict community events. Further, the model’s performance is enhanced using hyper-parameter tuning by selecting the appropriate parameters. Evolutionary algorithms are effective in tuning these hyper-parameters. This paper investigates the effectiveness of Cuckoo search optimization (CSO), particle swarm optimization (PSO), ant colony optimization (ACO), jellyfish search optimization (JFO), and mayfly optimization (MFO) evolutionary algorithms in tuning the hyper-parameters of four ML models to achieve higher accuracy in the results. The comparative analysis of these 20 combinations (five evolutionary algorithms and four ML models) shows that CSO improves average accuracy by 4.12% in all the machine learning models compared to PSO, ACO, JFO, and MFO. Furthermore, results confirm that CSO precisely suits the neural network model in tuning its hyper-parameters. The accuracy of the neural network model improved by 4.5% after tuning its hyper-parameters using CSO.

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Metadata
Title
Application of Evolutionary Algorithms in Social Networks: A Comparative Machine Learning Perspective
Authors
B. S. A. S. Rajita
Pranay Tarigopula
Phanindra Ramineni
Ashank Sharma
Subhrakanta Panda
Publication date
04-04-2023
Publisher
Springer Japan
Published in
New Generation Computing / Issue 2/2023
Print ISSN: 0288-3635
Electronic ISSN: 1882-7055
DOI
https://doi.org/10.1007/s00354-023-00215-4

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