Multiple Biases-incorporated Latent Factorization of Tensors for Dynamic Network Link Prediction

Abstract

The topological information of a dynamic network varies over time, making it crucial to capture its temporal patterns for predicting missing links accurately. A latent factorization of tensors (LFT)-based model has proven to be efficient to solve this problem, where a dynamic network is represented as a three-way high-dimensional and sparse (HiDS) tensor. However, currently LFT-based models do not consider multiple biases in analyzing an HiDS tensor for accomplishing dynamic link prediction. To address this issue, this paper proposes a multiple biases-incorporated latent factorization of tensors (MBLFT) model, which integrates short-term bias, preprocessing bias and long-term bias into an LFT model. Empirical studies on two large-scale dynamic networks from real applications show that compared with state-of-the-art predictors, an MBLFT model achieves higher prediction accuracy and computational efficiency for missing links in dynamic network.