论文标题
评估具有对比学习特征的图形生成模型
Evaluating Graph Generative Models with Contrastively Learned Features
论文作者
论文摘要
已经为图形生成模型提出了广泛的模型,需要采用有效的方法来评估其质量。到目前为止,大多数技术都使用基于子图计数的传统指标,或者使用随机初始化的图形神经网络(GNN)的表示。我们建议使用违反训练的GNN而不是随机GNN的表示形式,并表明这给出了更可靠的评估指标。但是,传统方法和基于GNN的方法都没有主导另一种方法:我们举例说明每种方法无法区分的示例。我们证明了图形子结构网络(GSN),以一种结合两种方法的方式,可以更好地区分图形数据集之间的距离。
A wide range of models have been proposed for Graph Generative Models, necessitating effective methods to evaluate their quality. So far, most techniques use either traditional metrics based on subgraph counting, or the representations of randomly initialized Graph Neural Networks (GNNs). We propose using representations from contrastively trained GNNs, rather than random GNNs, and show this gives more reliable evaluation metrics. Neither traditional approaches nor GNN-based approaches dominate the other, however: we give examples of graphs that each approach is unable to distinguish. We demonstrate that Graph Substructure Networks (GSNs), which in a way combine both approaches, are better at distinguishing the distances between graph datasets.