Lin-Yijie/Graph-Matching-Networks
PyTorch implementation of Graph Matching Networks, e.g., Graph Matching with Bi-level Noisy Correspondence (COMMON, ICCV 2023), Graph Matching Networks for Learning the Similarity of Graph Structured Objects (GMN, ICML 2019).
This helps researchers in fields like computer vision or bioinformatics determine how similar two complex, graph-structured objects are, even when there's noise or slight variations between them. You provide descriptions of two objects structured as graphs, and it outputs a score indicating their similarity and potentially how their parts correspond. This is for scientists or engineers who work with structured data like molecular graphs, social networks, or image features represented as graphs.
320 stars.
Use this if you need to compare two complex items that can be represented as graphs and find correspondences between their components, especially when facing imperfect or noisy data.
Not ideal if your data is simple, unstructured, or if you only need a basic comparison without detailed correspondence mapping.
Stars
320
Forks
60
Language
Python
License
—
Category
Last pushed
Mar 13, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Lin-Yijie/Graph-Matching-Networks"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
a-r-j/graphein
Protein Graph Library
raamana/graynet
Subject-wise networks from structural MRI, both vertex- and voxel-wise features (thickness, GM...
pykale/pykale
Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for...
dmlc/dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.