论文标题
GPN：图形神经网络的联合结构学习框架
GPN: A Joint Structural Learning Framework for Graph Neural Networks
论文作者
论文摘要
图形神经网络（GNN）已应用于各种图形任务。 GNN的大多数现有工作都是基于以下假设：给定的图数据是最佳的，而在图形数据中不可避免地存在培训图中缺少或不完整的边缘，从而导致性能退化。在本文中，我们提出了生成预测网络（GPN），这是一个基于GNN的关节学习框架，同时学习图形结构和下游任务。具体而言，我们为此联合学习任务开发了一个双重优化框架，其中上层优化（生成器）和较低优化（预测指标）均与GNN实例化。据我们所知，我们的方法是解决此任务的第一个基于GNN的二元优化框架。通过广泛的实验，我们的方法使用基准数据集优于广泛的基准。
Graph neural networks (GNNs) have been applied into a variety of graph tasks. Most existing work of GNNs is based on the assumption that the given graph data is optimal, while it is inevitable that there exists missing or incomplete edges in the graph data for training, leading to degraded performance. In this paper, we propose Generative Predictive Network (GPN), a GNN-based joint learning framework that simultaneously learns the graph structure and the downstream task. Specifically, we develop a bilevel optimization framework for this joint learning task, in which the upper optimization (generator) and the lower optimization (predictor) are both instantiated with GNNs. To the best of our knowledge, our method is the first GNN-based bilevel optimization framework for resolving this task. Through extensive experiments, our method outperforms a wide range of baselines using benchmark datasets.
