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Social Network Clustering Enhancement by using Imperial Competitive Evolutionary Algorithm and Inter-Similarity of Network Nodes
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Abstract: (1196 Views) |
Due to the growing desire of people to join and use social networks, communication and sharing data in these networks has been considered by various sciences such as political science, psychology, sociology, economics, etc. Hence, researchers have begun to distinguish and extract relationships between individuals from the data contained in these networks, to create more accurate communities. However, there is still no effective method to identify and extract communities based on social media data.
In this article, a method has been proposed for social network accurate clustering by using Imperial Competitive Evolutionary Algorithm (ICEA) and selecting the initial population based on the density-based clustering criterion. The proposed method has improved the result of modularity about 21.45% in average, compared to rival basic ICEA and extracted more densed communities. |
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| Keywords: Imperial Competitive Algorithm, Evolutionary Algorithms, Graph Clustering, density-based clustering, Social Networks |
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Full-Text [PDF 4903 kb]
(227 Downloads)
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Type of Study: Scientific-extension |
Subject:
Special
Received: 2021/08/2 | Accepted: 2021/08/1 | Published: 2021/08/1
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