基于自适应神经网络的图结构学习

Abstract

This paper introduces a novel neural network architecture, termed "Adaptive Graph Neural Network" (AGN), that leverages dynamic graph structure learning to enhance data modeling and information extraction. Traditional neural networks typically employ static graph structures, limiting their ability to effectively capture complex relationships within data. AGN dynamically adjusts the graph structure during training, adapting to the specific characteristics of the data. This adaptive approach, combined with a novel graph-based representation learning technique, results in improved performance across various data analysis tasks. We present experimental results demonstrating the superior performance of AGN compared to conventional models. The core mechanism focuses on iterative graph refinement guided by a learned loss function that prioritizes information extraction from the graph. This paper explores the potential of dynamically structured neural networks to unlock new capabilities in data analysis and modeling.