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- W4387143713 abstract "Social network analysis is an emerging field of research that is an amalgamation of various disciplines such as social science, network science, data analytics and behavioral science. It enables us to investigate the microscopic as well as macroscopic characteristics of real-world entities and their connectedness. One of the most viable tools for analyzing the structural patterns and motifs in social networks is community detection. In this research, we aim to reveal overlapping community patterns in social networks by employing seed expansion and fuzzification. The proposed approach consists of into three phases: selecting appropriate seeds that will act as community centers to initiate community expansion, assigning membership values to community nodes, and finally updating community centers with the most central member of the community. The performance of the proposed approach is examined on synthetic datasets as well as real-world dataset. The obtained results establish the efficacy of the proposed approach." @default.
- W4387143713 created "2023-09-29" @default.
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- W4387143713 date "2023-08-03" @default.
- W4387143713 modified "2023-10-18" @default.
- W4387143713 title "A Fuzzified Approach for Overlapping Community Detection in Social Networks Based on Nodes Similarity" @default.
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- W4387143713 doi "https://doi.org/10.1145/3607947.3608086" @default.
- W4387143713 hasPublicationYear "2023" @default.
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