基于自适应的图神经网络的拓扑学习

Abstract

This paper presents a novel approach to graph neural network (GNN) learning, leveraging an adaptive graph neural network (AGNN) to dynamically learn the graph topology and subsequently utilize this topology for data modeling. Traditional GNNs typically rely on static graph structures, limiting their ability to effectively capture complex relationships within the data. This work introduces an AGNN that adapts its network architecture and weights to the specific graph structure, enhancing the model's capacity to represent and extract meaningful information. We demonstrate the effectiveness of this method through several experiments, showcasing improved data modeling and analysis efficiency compared to existing approaches.