A Convolution Bias-Incorporated Nonnegative Latent Factorization of Tensors Model for Accurate Representation Learning to Dynamic Directed Graphs

A dynamic directed graph (DDG) can describe complex dynamic interactions among massive entities, for example, traffic transmissions in a metropolitan area network (MAN), in a natural way. Due to the rapid expansion of a network, it is impossible to capture all the interactions at each time slot, making a resultant DDG be high-dimensional and incomplete (HDI). A nonnegative latent factorization of tensors (NLFT) model has proven to be highly efficient in extracting desired knowledge from an HDI DDG. Nevertheless, an existing NLFT model attempts to be easily affected by the instantaneous data fluctuations. Motivated by this discovery, this article innovatively proposes a convolution bias-incorporated NLFT (CB-NLFT) model with threefold ideas: 1) utilizing the Tucker decomposition framework for accurately representing the complex patterns hidden in an HDI DDG; 2) establishing a novel convolution bias scheme for precisely depicting the instantaneous data fluctuations; and 3) theoretically proving the CB-NLFT model’s convergence. Extensively empirical studies on six real-world datasets illustrate that the proposed CB-NLFT achieves significantly higher accuracy and computational efficiency when addressing the representation learning to a DDG in comparison with state-of-the-art models.