论文标题
SEEDGNN：用于监督种子图匹配的图形神经网络
SeedGNN: Graph Neural Networks for Supervised Seeded Graph Matching
论文作者
论文摘要
对于种子图匹配的设计图形神经网络（GNN）的兴趣越来越越来越大，该图匹配的目的是仅使用拓扑信息和一小部分种子节点匹配两个未标记的图。但是，此任务以前的大多数GNN都使用半监督的方法，该方法需要大量种子，无法学习可转移到看不见的图形的知识。相比之下，本文提出了一种新的监督方法，该方法可以从培训集中学习如何与几个种子相匹配。我们的Seedgnn架构结合了几种新型设计，灵感来自种子图匹配的理论研究：1）它可以学习从不同啤酒花中计算和使用类似见证的信息，以一种可以推广到不同尺寸的图形的方式； 2）它可以使用易于匹配的节点对作为新种子来改善随后的层中的匹配。我们在合成和现实世界图上评估了SEEDGNN，并在现有文献中表现出对非学习和学习算法的显着改善。此外，我们的实验证实，从训练图中学到的知识可以推广到测试不同大小和类别的图表。
There is a growing interest in designing Graph Neural Networks (GNNs) for seeded graph matching, which aims to match two unlabeled graphs using only topological information and a small set of seed nodes. However, most previous GNNs for this task use a semi-supervised approach, which requires a large number of seeds and cannot learn knowledge that is transferable to unseen graphs. In contrast, this paper proposes a new supervised approach that can learn from a training set how to match unseen graphs with only a few seeds. Our SeedGNN architecture incorporates several novel designs, inspired by theoretical studies of seeded graph matching: 1) it can learn to compute and use witness-like information from different hops, in a way that can be generalized to graphs of different sizes; 2) it can use easily-matched node-pairs as new seeds to improve the matching in subsequent layers. We evaluate SeedGNN on synthetic and real-world graphs and demonstrate significant performance improvements over both non-learning and learning algorithms in the existing literature. Furthermore, our experiments confirm that the knowledge learned by SeedGNN from training graphs can be generalized to test graphs of different sizes and categories.
