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NICASN: Non-negative Matrix Factorization and Independent Component Analysis for Clustering Social Networks

Published onMay 27, 2022
NICASN: Non-negative Matrix Factorization and Independent Component Analysis for Clustering Social Networks
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Abstract

Discovering clusters in social networks is of fundamental and practical interest. This paper presents a novel clustering strategy for large-scale highly-connected social networks. We propose a new hybrid clustering technique based on non-negative matrix factorization and independent component analysis for finding complex relationships among users of a huge social network. We extract the important features of the network and then
perform clustering on independent and important components of the network. Moreover, we introduce a new k-means centroid initialization method by which we achieve higher efficiency. We apply our approach on four well-known social networks: Facebook, Twitter, Academia and Youtube. We experimentally show that our approach achieves much better results in terms of the Silhouette coefficient compared to well-known counterparts
such as Hierarchical Louvain, Multiple Local Community detection, and k-means++


Article ID: 2022L10

Month: May

Year: 2022

Address: Online

Venue: Canadian Conference on Artificial Intelligence

Publisher: Canadian Artificial Intelligence Association

URL: https://caiac.pubpub.org/pub/kppr726k


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