Download PDFOpen PDF in browserNICASN: Non-Negative Matrix Factorization and Independent Component Analysis for Clustering Social NetworksEasyChair Preprint 799912 pages•Date: May 21, 2022AbstractDiscovering 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++. Keyphrases: Centroid initialization, Independent Component Analysis, NMF-k-means, dimensionality reduction, network clustering, non-negative matrix factorization
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