Community Detection Approach Via Graph Regularized Non-Negative Matrix Factorization

One of the most important problems towards studying complicated networks is community detection. The methodology of non-negative matrix factorization (NMF) has lately emerged as among the hottest research issues within community detection because of its ability to reveal natural structures and trends in high-dimensional information. The primary difficulty is that most community detection methods are hampered by the issue of nodes belonging to several communities. We will use the NMF technique in this work to tackle this issue by creating a novel mathematical function. In addition, we will include a regularized factor for simulating latent embedding space and a correlation factor to prevent overlap inside nodes that belong to various communities. Following that, the entire objective function will employ an optimization approach to arrive at the variable values that are ideal. Finally, we assess the effectiveness of different methodologies on real networks. According to experimental findings, the introduced approach is better among the other state-of-the-art methodology.