A large-scale dynamic network (LDN) is a source of data in many big data-related applications due to their large number of entities and large-scale dynamic interactions. They can be modeled as a high-dimensional incomplete (HDI) tensor that contains a wealth of knowledge about time patterns. A Latent factorization of tensors (LFT) model efficiently extracts this time pattern, which can be established using stochastic gradient descent (SGD) solvers. However, LFT models based on SGD are often limited by training schemes and have poor tail convergence. To solve this problem, this paper proposes a novel nonlinear LFT model (MNNL) based on momentum-incorporated SGD, which extracts non-negative latent factors from HDI tensors to make training unconstrained and compatible with general training schemes, while improving convergence accuracy and speed. Empirical studies on two LDN datasets show that compared to existing models, the MNNL model has higher prediction accuracy and convergence speed.

翻译：大规模动态网络（LDN）因具有大量实体和大规模动态交互，成为众多大数据相关应用的数据源。它们可被建模为包含丰富时间模式知识的高维不完全（HDI）张量。张量隐因子分解（LFT）模型通过随机梯度下降（SGD）求解器高效提取该时间模式。然而，基于SGD的LFT模型常受限于训练方案且尾部收敛性差。为解决此问题，本文提出一种基于动量SGD的新型非线性LFT模型（MNNL），其从HDI张量中提取非负隐因子，使训练无约束且兼容通用训练方案，同时提升收敛精度与速度。在两个LDN数据集上的实证研究表明：与现有模型相比，MNNL模型具有更高的预测精度与收敛速度。