Learning Accurate Representation to Nonstandard Tensors via a Mode-Aware Tucker Network

Abstract

A nonstandard tensor is frequently adopted to model a large-sale complex dynamic network. A Tensor Representation Learning (TRL) model enables extracting valuable knowledge form a dynamic network via learning low-dimensional representation of a target nonstandard tensor. Nevertheless, the representation learning ability of existing TRL models are limited for a nonstandard tensor due to its inability to accurately represent the specific nature of the nonstandard tensor, i.e., mode imbalance, high-dimension, and incompleteness. To address this issue, this study innovatively proposes a Mode-Aware Tucker Networkbased Tensor Representation Learning (MTN-TRL) model with three-fold ideas: a) designing a mode-aware Tucker network to accurately represent the imbalanced mode of a nonstandard tensor, b) building an MTN-based high-efficient TRL model that fuses both data density-oriented modeling principle and adaptive parameters learning scheme, and c) theoretically proving the MTN-TRL model's convergence. Extensive experiments on eight nonstandard tensors generating from real-world dynamic networks demonstrate that MTN-TRL significantly outperforms state-of-the-art models in terms of representation accuracy.