Community detection is one of the popular research topics in the field of complex networks
analysis. It aims to identify communities, represented as cohesive subgroups or clusters …

Non-negative matrix factorization (NMF) is the problem of determining two non-negative low
rank factors W and H, for the given input matrix A, such that A≈ WH. NMF is a useful tool for …

Due to the important role in analyzing the topological structure of complex networks,
community detection has attracted increasing attention recently. The network embedding …

Non-negative matrix factorization (NMF) is the problem of determining two non-negative low
rank factors Wand H, for the given input matrix A, such that A WH. NMF is a useful tool for …

The holistic analysis and understanding of the latent (that is, not directly observable)
variables and patterns buried in large datasets is crucial for data-driven science, decision …

Sparse matrix multiplication is traditionally performed in memory and scales to large
matrices using the distributed memory of multiple nodes. In contrast, we scale sparse matrix …

Nonnegative matrix factorization (NMF) has been successfully applied in several data
mining tasks. Recently, there is an increasing interest in the acceleration of NMF, due to its …

Background Matrix factorization is a well established pattern discovery tool that has seen
numerous applications in biomedical data analytics, such as gene expression co-clustering …

Nonnegative matrix factorization (NMF) has attracted much attention in the last decade as a
dimension reduction method in many applications. Due to the explosion in the size of data …

Non-negative matrix factorization (NMF), the problem of finding two non-negative low-rank
factors whose product approximates an input matrix, is a useful tool for many data mining …