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.
We tackle the problem of similarity learning for structured objects with applications in particular in computer security, and propose a new model graph matching networks that excels on this task. Authors introduce a Graph Matching Network for retrival and matching of graph structured objects. The authors attack the problem of graph matching by proposing an extension of graph embedding networks The authors present two methods for learning a similarity score between pairs of graphs and show the beneficiality of introducing idesa from graph matching to graph neural networks.