Autoregressive-incorporated Non-negative Latent Factorization of Tensors for Temporal Link Prediction in Cryptocurrency Transaction Network

Abstract

Cryptocurrency transaction networks (CTNs) are frequently encountered in real-world applications. Due to practical constraints, it is generally not feasible to observe complete interactions among all nodes at every time slot, leading to numerous missing links in CTNs. A link prediction model based on Non-negative Latent Factorization of Tensors (NLFT) has demonstrated effectiveness in predicting these missing links within a temporal network. However, existing NLFT models do not adequately capture the evolving relationships within a temporal network, limiting their ability to predict temporal links effectively. To address this limitation, this paper proposes an Autoregressive-incorporated Non-negative Latent-Factorization of Tensors (ArNLFT) model. The core idea is to adopt an autoregressive model to represent the evolving relationships in temporal networks, thereby constructing an autoregressive-incorporated objective function. Additionally, a non-negative parameter learning scheme, based on a single latent factor-dependent, non-negative, and multiplicative update rule, is designed to ensure the non-negativity of the proposed model. The effectiveness of the ArNLFT model is ultimately verified through temporal link prediction tasks on two real CTNs, with results showing that ArNLFT achieves a significant accuracy improvement compared to its peers.