A large-scale dynamically weighted directed network (DWDN) involving numerous entities
and massive dynamic interaction is an essential data source in many big-data-related …

A nonnegative latent factorization of tensors (NLFT) model precisely represents the temporal
patterns hidden in multichannel data emerging from various applications. It often adopts a …

Tensor train decomposition is a powerful representation for high-order tensors, which has
been successfully applied to various machine learning tasks in recent years. In this paper …

A Non-negative Latent-factorization-of-tensors model relying on a N onnegative and M
ultiplicative U pdate on I ncomplete T ensors (NMU-IT) algorithm facilitates efficient …

Tensor train (TT) decomposition has drawn people's attention due to its powerful
representation ability and performance stability in high-order tensors. In this paper, we …

The explosive growth in popularity of social networking leads to the problematic usage. An
increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship …

Tensor decomposition is a well-known tool for multiway data analysis. This work proposes
using stochastic gradients for efficient generalized canonical polyadic (GCP) tensor …

Complex biological systems can be described as a multitude of cell-cell interactions (CCIs).
Recent single-cell RNA-sequencing technologies have enabled the detection of CCIs and …

Analyzing multiway measurements with variations across one mode of the dataset is a
challenge in various fields including data mining, neuroscience, and chemometrics. For …

Tensor decompositions play an important role in a variety of applications, such as signal
processing and machine learning. In practice, the tensor can be incomplete or very large …