The Dynamic Communication Network (DCN) describes the interactions over time among various communication nodes, and it is widely used in Big-data applications as a data source. As the number of communication nodes increases and temporal slots accumulate, each node interacts in with only a few nodes in a given temporal slot, the DCN can be represented by an High-Dimensional Sparse (HDS) tensor. In order to extract rich behavioral patterns from an HDS tensor in DCN, this paper proposes an Adaptive Temporal-dependent Tensor low-rank representation (ATT) model. It adopts a three-fold approach: a) designing a temporal-dependent method to reconstruct temporal feature matrix, thereby precisely represent the data by capturing the temporal patterns; b) achieving hyper-parameters adaptation of the model via the Differential Evolutionary Algorithms (DEA) to avoid tedious hyper-parameters tuning; c) employing nonnegative learning schemes for the model parameters to effectively handle an the nonnegativity inherent in HDS data. The experimental results on four real-world DCNs demonstrate that the proposed ATT model significantly outperforms several state-of-the-art models in both prediction errors and convergence rounds.

翻译：动态通信网络描述了不同通信节点随时间推移的交互关系，作为数据源广泛应用于大数据应用中。随着通信节点数量的增加和时间槽的积累，每个节点在给定时间槽内仅与少数节点交互，动态通信网络可表示为高维稀疏张量。为从动态通信网络的高维稀疏张量中提取丰富的行为模式，本文提出一种自适应时间依赖张量低秩表示模型。该模型采用三重策略：a) 设计时间依赖方法重构时序特征矩阵，通过捕捉时序模式精确表征数据；b) 利用差分进化算法实现模型超参数自适应，避免繁琐的超参数调优；c) 对模型参数采用非负学习方案，有效处理高维稀疏数据固有的非负特性。在四个真实动态通信网络上的实验结果表明，所提模型在预测误差和收敛轮数方面均显著优于多种先进模型。