# Multilevel Graph Matching Networks for Deep Graph Similarity Learning

arXiv: https://arxiv.org/abs/2007.04395 · HTML: https://mlsanity.com/paper/2007.04395/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

## Built on by (most cited first, 29 of 29)

- [Efficient Graph Similarity Computation with Alignment Regularization](https://mlsanity.com/paper/2406.14929/) (2024)
- [Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2112.12310/) (2021)
- [CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning](https://mlsanity.com/paper/2205.15083/) (2022)
- [More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference](https://mlsanity.com/paper/2208.04580/) (2022)
- [The quest for the GRAph Level autoEncoder (GRALE)](https://mlsanity.com/paper/2505.22109/) (2025)
- [Variational Graph Generator for Multi-View Graph Clustering](https://mlsanity.com/paper/2210.07011/) (2022)
- [Recent Advances in Malware Detection: Graph Learning and Explainability](https://mlsanity.com/paper/2502.10556/) (2025)
- [State of the Art and Potentialities of Graph-level Learning](https://mlsanity.com/paper/2301.05860/) (2023)
- [Graph Neural Networks for Databases: A Survey](https://mlsanity.com/paper/2502.12908/) (2025)
- [SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification](https://mlsanity.com/paper/2110.04494/) (2021)
- [Graph similarity learning for change-point detection in dynamic networks](https://mlsanity.com/paper/2203.15470/) (2022)
- [Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning](https://mlsanity.com/paper/2210.10643/) (2022)
- [Towards Unsupervised Training of Matching-based Graph Edit Distance Solver via Preference-aware GAN](https://mlsanity.com/paper/2506.01977/) (2025)
- [Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs](https://mlsanity.com/paper/2506.12025/) (2025)
- [Constructing Contrastive samples via Summarization for Text Classification with limited annotations](https://mlsanity.com/paper/2104.05094/) (2021)
- [Privacy-Preserved Neural Graph Similarity Learning](https://mlsanity.com/paper/2210.11730/) (2022)
- [DiffGED: Computing Graph Edit Distance via Diffusion-based Graph Matching](https://mlsanity.com/paper/2503.18245/) (2025)
- [Dual Explanations via Subgraph Matching for Malware Detection](https://mlsanity.com/paper/2504.20904/) (2025)
- [GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance](https://mlsanity.com/paper/2605.16668/) (2026)
- [Graph Edit Distance Learning via Different Attention](https://mlsanity.com/paper/2308.13871/) (2023)
- [FuncEvalGMN: Evaluating Functional Correctness of SQL via Graph Matching Network](https://mlsanity.com/paper/2407.14530/) (2024)
- [Multimodal Alignment of Histopathological Images Using Cell Segmentation and Point Set Matching for Integrative Cancer Analysis](https://mlsanity.com/paper/2410.00152/) (2024)
- [GIMS: Image Matching System Based on Adaptive Graph Construction and Graph Neural Network](https://mlsanity.com/paper/2412.18221/) (2024)
- [Neural Network Graph Similarity Computation Based on Graph Fusion](https://mlsanity.com/paper/2502.18291/) (2025)
- [Finding Counterfactual Evidences for Node Classification](https://mlsanity.com/paper/2505.11396/) (2025)
- [SEGMN: A Structure-Enhanced Graph Matching Network for Graph Similarity Learning](https://mlsanity.com/paper/2411.03624/) (2024)
- [HeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity](https://mlsanity.com/paper/2503.08739/) (2025)
- [GED-Consistent Disentanglement of Aligned and Unaligned Substructures for Graph Similarity Learning](https://mlsanity.com/paper/2511.19837/) (2025)
- [Through the PRISm: Importance-Aware Scene Graphs for Image Retrieval](https://mlsanity.com/paper/2512.18407/) (2025)

## Abstract (arXiv)

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.