"Non-negative matrix factorization"

Local Structure and High-Order Feature Preserved Network Embedding Based on Non-Negative Matrix Factorization

Network embedding, as an effective method of learning the low-dimensional representations of nodes, has been widely applied to various complex network analysis tasks, such as node classification, community detection, link prediction and evolution …

A Novel Evolutionary Clustering via the First-Order Varying Information for Dynamic Networks

Temporal community detection could help us analyze and understand the meaningful substructure hidden within dynamic networks in the real world. Evolutionary clustering, as a popular framework for clustering stream data, has been denoted for mining …

A Perturbation-Based Framework for Link Prediction via Non-Negative Matrix Factorization

Many link prediction methods have been developed to infer unobserved links or predict latent links based on the observed network structure. However, due to network noises and irregular links in real network, the performances of existed methods are …

Autonomous Overlapping Community Detection in Temporal Networks: A Dynamic Bayesian Nonnegative Matrix Factorization Approach

A wide variety of natural or artificial systems can be modeled as time-varying or temporal networks. To understand the structural and functional properties of these time-varying networked systems, it is desirable to detect and analyze the evolving …