论文标题
定制的图神经网络
Customized Graph Neural Networks
论文作者
论文摘要
最近，图神经网络（GNN）大大提出了图形分类的任务。通常，我们首先在给定的训练集中构建了带有图形的统一GNN模型，然后使用此统一模型预测测试集中所有看不见的图形的标签。但是，同一数据集中的图通常具有截然不同的结构，这表明统一模型可以在给定单个图的情况下是优化的。因此，在本文中，我们旨在开发自定义的图形神经网络以进行图形分类。具体来说，我们提出了一个新颖的自定义图形神经网络框架，即自定义-GNN。给定图形样本，自定义-GNN可以根据其结构为该图生成特定于该图的样品模型。同时，所提出的框架非常通用，可以应用于众多现有的图形神经网络模型。各种图分类基准的全面实验证明了所提出的框架的有效性。
Recently, Graph Neural Networks (GNNs) have greatly advanced the task of graph classification. Typically, we first build a unified GNN model with graphs in a given training set and then use this unified model to predict labels of all the unseen graphs in the test set. However, graphs in the same dataset often have dramatically distinct structures, which indicates that a unified model may be sub-optimal given an individual graph. Therefore, in this paper, we aim to develop customized graph neural networks for graph classification. Specifically, we propose a novel customized graph neural network framework, i.e., Customized-GNN. Given a graph sample, Customized-GNN can generate a sample-specific model for this graph based on its structure. Meanwhile, the proposed framework is very general that can be applied to numerous existing graph neural network models. Comprehensive experiments on various graph classification benchmarks demonstrate the effectiveness of the proposed framework.
