# Graph Matching Networks for Learning the Similarity of Graph Structured Objects

arXiv: https://arxiv.org/abs/1904.12787 · HTML: https://mlsanity.com/paper/1904.12787/

## Key facts

- Cited by 205 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2019.

## Built on by (most cited first, 30 of 205)

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- [Efficient Graph Similarity Computation with Alignment Regularization](https://mlsanity.com/paper/2406.14929/) (2024)
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- [A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications](https://mlsanity.com/paper/2308.16375/) (2023)
- [Graph Self-supervised Learning with Accurate Discrepancy Learning](https://mlsanity.com/paper/2202.02989/) (2022)
- [Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks](https://mlsanity.com/paper/2211.03232/) (2022)
- [Equivariant Graph Attention Networks for Molecular Property Prediction](https://mlsanity.com/paper/2202.09891/) (2022)
- [Automated Data Augmentations for Graph Classification](https://mlsanity.com/paper/2202.13248/) (2022)
- [VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment](https://mlsanity.com/paper/2210.04135/) (2022)
- [FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction](https://mlsanity.com/paper/2305.02549/) (2023)
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- [Geodesic Graph Neural Network for Efficient Graph Representation Learning](https://mlsanity.com/paper/2210.02636/) (2022)

## Abstract (arXiv)

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.