自适应图神经网络的图结构调整

Abstract

This paper introduces a novel algorithm for graph neural network (GNN) graph structure adjustment, leveraging the principles of adaptive neural networks. Traditional approaches often struggle with fixed parameter settings, limiting the model's ability to effectively capture complex relationships within the data. Our proposed method dynamically adjusts graph structure parameters based on input data characteristics, offering a significant improvement in performance compared to existing techniques. The core mechanism involves iteratively refining the graph's connectivity and node features, guided by a feedback loop that optimizes the model's predictive capabilities. We demonstrate the efficacy of our algorithm through extensive experiments on diverse datasets, showcasing enhanced accuracy and robustness in various GNN tasks. The paper concludes with a discussion of the theoretical implications and potential future directions for this approach.