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

TL;DR

This paper introduces an adaptive tensor factorization model for dynamic communication networks that captures temporal patterns, automatically tunes hyperparameters, and effectively handles sparse, nonnegative data, leading to improved prediction accuracy.

Contribution

It proposes a novel adaptive tensor low-rank representation model with hyper-parameter optimization and nonnegative learning for analyzing dynamic communication networks.

Findings

Significantly outperforms existing models in prediction accuracy.

Reduces convergence rounds in experiments.

Effectively captures temporal patterns in HDS tensors.

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.