An enforced non-negative matrix factorization based approach towards community detection in dynamic networks

Identifying community structures within network dynamics is important for analysing the latent structure of the network, understanding the functions of the network, predicting the evolution of the network as well as detecting unusual events of the network. From various perspectives, a diversity of approaches towards dynamic community detection has been advised. However, owing to the difficulty in parameter adjustment, high temporal complexity and detection accuracy is diminishing as time slice rises; and recognizing the community composition in dynamic networks gets extremely complex. The basic models, principles, qualities, and techniques of latent factor models, as well as their various modifications, generalizations and extensions, are summed up systematically in this study which focuses on both theoretical and experimental research into latent factor models across the latest ten years. Latent factor model like non-negative matrix factorization is considered one of the most successful models for community identification which aims to uncover distributed lower dimension representation so as to reveal community node membership. These models are mostly centred on reconstructing the network from node representations while requiring the representation to have special desirable qualities (non-negativity). The purpose of this work is to provide an experimental as well as theoretical comparative analysis of the latent factor approaches employed to detect communities within dynamic networks. Parallelly we have devised the generic and improved non-negative matrix factorization-based model which will help in producing robust community detection results in dynamic networks. The results have been calculated from the experiments done in Python. Moreover our models methodology focuses on information dynamics so as to quantify the information propagation among the involved nodes unlike existing methods that considers networks first-order topological information described by its adjacency matrix without considering the information propagation between the nodes. In addition, this paper intends to create a unified, state of the art framework meant for non-negative matrix factorization conception which could be useful for future study.