An Adaptive Latent Factorization of Tensors Model for Embedding Dynamic Communication Network

• URL: http://arxiv.org/abs/2408.16573v1
• Date: Thu, 29 Aug 2024 14:40:32 GMT
• Title: An Adaptive Latent Factorization of Tensors Model for Embedding Dynamic Communication Network
• Authors: Xin Liao, Qicong Hu, Peng Tang,
• Abstract summary: The Dynamic Communication Network (DCN) describes the interactions over time among various communication nodes. This paper proposes an Adaptive Temporal-dependent low-rank representation model (ATT) 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.
• Score: 15.577058568902272
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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