We present a simple and yet effective interpolation-based regularization
technique, aiming to improve the generalization of Graph Neural Networks (GNNs)
on supervised graph classification. We leverage Mixup, an effective regularizer
for vision, where random sample pairs and their labels are interpolated to
create synthetic images for training. Unlike images with grid-like coordinates,
graphs have arbitrary structure and topology, which can be very sensitive to
any modification that alters the graph's semantic meanings. This posts two
unanswered questions for Mixup-like regularization schemes: Can we directly mix
up a pair of graph inputs? If so, how well does such mixing strategy regularize
the learning of GNNs? To answer these two questions, we propose ifMixup, which
first adds dummy nodes to make two graphs have the same input size and then
simultaneously performs linear interpolation between the aligned node feature
vectors and the aligned edge representations of the two graphs. We empirically
show that such simple mixing schema can effectively regularize the
classification learning, resulting in superior predictive accuracy to popular
graph augmentation and GNN methods.

本文提出 ifMixup，一种基于插值的正则化技术，通过在输入的节点特征向量和边表示之间进行线性插值来改善 GNN 在监督图分类中的泛化能力，该解决方案使用 Mixup 在图像上的高效策略进行设计，并添加虚拟节点来使两个图形拥有相同的输入大小，实验结果表明 ifMixup 显著改善了预测准确率，优于流行的图形增强和 GNN 方法。

ifMixup：插值图对正则化图分类

ifMixup: Interpolating Graph Pair to Regularize Graph Classification

Recent works leveraging Graph Neural Networks to approach graph matching
tasks have shown promising results. Recent progress in learning discrete
distributions poses new opportunities for learning graph matching models. In
this work, we propose a new model, Stochastic Iterative Graph MAtching (SIGMA),
to address the graph matching problem. Our model defines a distribution of
matchings for a graph pair so the model can explore a wide range of possible
matchings. We further introduce a novel multi-step matching procedure, which
learns how to refine a graph pair's matching results incrementally. The model
also includes dummy nodes so that the model does not have to find matchings for
nodes without correspondence. We fit this model to data via scalable stochastic
optimization. We conduct extensive experiments across synthetic graph datasets
as well as biochemistry and computer vision applications. Across all tasks, our
results show that SIGMA can produce significantly improved graph matching
results compared to state-of-the-art models. Ablation studies verify that each
of our components (stochastic training, iterative matching, and dummy nodes)
offers noticeable improvement.

本文提出了一种新的基于图神经网络的图匹配方法，名为 Stochastic Iterative Graph MAtching (SIGMA)，该方法采用多步匹配程序和虚拟节点等创新技术，并采用可伸缩随机优化方法，经过大量实验验证表明，与现有领先方法相比，该方法在合成图数据集、生物化学和计算机视觉应用中，均能显著提高图匹配的结果。