Effective Detection of Modular and Granular Overlaps in Online Social Networks Using Fuzzy ART

Effective Detection of Modular and Granular Overlaps in Online Social Networks Using Fuzzy ART There has been lot of research endeavours in detecting overlapping community structures in complex networks. This paper concentrates on one of the most popular complex networks of recent times—online social networks. Some of the existing methodologies in community detection for online social networks are discussed. The goal of the proposed algorithm is to solve the stability–plasticity dilemma and to suggest a new technique for detecting modular and granular overlaps in community detection. The stability–plasticity dilemma is solved using a Fuzzy ART-inspired algorithm for overlapping community detection for detecting modular and granular overlaps. The algorithm is designed by making use of network measures such as vertex betweenness, betweenness centrality, and split betweenness. The validation metrics used for testing the algorithm were sensitivity, specificity, accuracy and normalized mutual information. The algorithm has been tested and validated using benchmark datasets and real network datasets and gives a cumulative performance of 3.1/4.0. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Fuzzy Systems Springer Journals

Effective Detection of Modular and Granular Overlaps in Online Social Networks Using Fuzzy ART

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Springer Berlin Heidelberg
Copyright © 2016 by Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Operations Research, Management Science
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