While the celebrated graph neural networks yield effective representations
for individual nodes of a graph, there has been relatively less success in
extending to the task of graph similarity learning. Recent work on graph
similarity learning has considered either global-level graph-graph interactions
or low-level node-node interactions, however ignoring the rich cross-level
interactions (e.g., between each node of one graph and the other whole graph).
In this paper, we propose a multi-level graph matching network (MGMN) framework
for computing the graph similarity between any pair of graph-structured objects
in an end-to-end fashion. In particular, the proposed MGMN consists of a
node-graph matching network for effectively learning cross-level interactions
between each node of one graph and the other whole graph, and a siamese graph
neural network to learn global-level interactions between two input graphs.
Furthermore, to compensate for the lack of standard benchmark datasets, we have
created and collected a set of datasets for both the graph-graph classification
and graph-graph regression tasks with different sizes in order to evaluate the
effectiveness and robustness of our models. Comprehensive experiments
demonstrate that MGMN consistently outperforms state-of-the-art baseline models
on both the graph-graph classification and graph-graph regression tasks.
Compared with previous work, MGMN also exhibits stronger robustness as the
sizes of the two input graphs increase.

本研究提出了一种多层图匹配网络框架，其中包含节点 - 图匹配网络和连体图神经网络，可在纵横交错的多层交互中精确地计算图状对象之间的相似度，并证明其在图分类和回归任务中的有效性及稳健性。

多层图匹配网络用于深度图相似度学习

Multilevel Graph Matching Networks for Deep Graph Similarity Learning

This paper addresses the challenging problem of retrieval and matching of
graph structured objects, and makes two key contributions. First, we
demonstrate how Graph Neural Networks (GNN), which have emerged as an effective
model for various supervised prediction problems defined on structured data,
can be trained to produce embedding of graphs in vector spaces that enables
efficient similarity reasoning. Second, we propose a novel Graph Matching
Network model that, given a pair of graphs as input, computes a similarity
score between them by jointly reasoning on the pair through a new cross-graph
attention-based matching mechanism. We demonstrate the effectiveness of our
models on different domains including the challenging problem of
control-flow-graph based function similarity search that plays an important
role in the detection of vulnerabilities in software systems. The experimental
analysis demonstrates that our models are not only able to exploit structure in
the context of similarity learning but they can also outperform domain-specific
baseline systems that have been carefully hand-engineered for these problems.

本文提出了两个关键性贡献：首先是演示了图神经网络如何训练并嵌入向量空间，以便有效地进行相似性推理；其次是提出了一种新的图匹配网络模型，通过新的跨图注意力匹配机制，在一对图之间共同推理，计算它们之间的相似度得分。在不同领域的实验分析表明，本文提出的模型不仅能够在相似性学习的上下文中利用结构，而且能够优于针对此类问题精心设计的基线系统。