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A Novel Approach for Tenuous Community Detection in Social Networks
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Author(s): Muhammad Asif (University of Lahore, Pakistan), Hassan Raza (University of Lahore, Pakistan)and Muhammad Imran Manzoor (University of Lahore, Pakistan)
Copyright: 2022
Volume: 3
Issue: 1
Pages: 12
Source title:
International Journal of Data Analytics (IJDA)
Editor(s)-in-Chief: Bruce Qiang Swan (SUNY Buffalo State, USA)
DOI: 10.4018/IJDA.297518
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
Tenuous community detection in social networks is becoming an interesting area of research and its various important applications are emerging. Existing research has focused on the identification of a dense community. However, finding the tenuous community has posed a different kinds of challenges and existing solutions are not viable for this research problem. In this paper, a novel approach called the least linked community (LLC) is proposed to find a tenuous community from online social networks. The concept of k-links with the shortest path distance between two nodes is proposed and utilized to find a community with the least interaction and weak relationships.The experiment demonstrated significant results in terms of accuracy, effectiveness, and efficiency compared to other existing techniques. Although the proposed algorithm presents significant results it may require further evaluation with different data sets.
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