A Proximal-ADMM-Incorporated Nonnegative Latent-Factorization-of-Tensors Model for Representing Dynamic Cryptocurrency Transaction Network

【Author】 Liao, Xin; Wu, Hao; He, Tiantian; Luo, Xin

【Source】IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

【影响因子】11.471

【Abstract】Cryptocurrency services, as one of the most successful applications of blockchain technology, have recently garnered significant attention from the graph learning community. Its large-scale dynamic transaction records contain a variety of behavioral patterns and rich knowledge involving accounts, making the dynamic cryptocurrency transaction network embedding (DCTNE) a hot, yet thorny research topic. As the trading accounts increase and time accumulates, considerable transaction services are dispersed into various time slots, leading to very sparse transaction data within a time slot, that is, the transaction service data is high-dimensional and incomplete (HDI). To efficiently mine high-value knowledge from HDI data, this article proposes a proximal-ADMM-incorporated nonnegative latent-factorization-of-tensors (PNL) model for DCTNE that adopts threefold ideas: 1) incorporating the proximal terms into the alternating-direction-method-of-multipliers (ADMMs)-based learning scheme to reduce the oscillations for high estimation accuracy and fast convergence; 2) implementing a parallel training process with hyperparameter self-adaptation for high computational efficiency; and 3) proving that the proximal-incorporated learning scheme guarantees the convergence to a Karush-Kuhn-Tucker (KKT) stationary point. Experimental results on eight real-world DCTNs show that the PNL significantly outperforms several state-of-the-art (SOTA) models, demonstrating not only high efficiency and accuracy in performing DCTNE, but also strong potential to enhance the operational reliability and stability of cryptocurrency transaction systems.

【Keywords】Cryptocurrency; Tensors; Convergence; Computational modeling; Adaptation models; Accuracy; Vectors; Representation learning; Blockchains; Spatiotemporal phenomena; Cryptocurrency service; dynamic cryptocurrency transaction network (DCTN); graph learning; high-dimensional and incomplete (HDI) data; network embedding; nonnegative latent-factorization-of-tensors (NLFTs); proximal alternating direction method of multipliers

【发表时间】2025 2025 SEP 5

【收录时间】2025-09-27

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