mlsanityData to 7 Oct 2026

Neural Bipartite Matching

arXiv 2005.11304 ↗First posted 22 May 2020cs.LG stat.MLcited by 16

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Abstract (arXiv)

Graph neural networks (GNNs) have found application for learning in the space of algorithms. However, the algorithms chosen by existing research (sorting, Breadth-First search, shortest path finding, etc.) usually align perfectly with a standard GNN architecture. This report describes how neural execution is applied to a complex algorithm, such as finding maximum bipartite matching by reducing it to a flow problem and using Ford-Fulkerson to find the maximum flow. This is achieved via neural execution based only on features generated from a single GNN. The evaluation shows strongly generalising results with the network achieving optimal matching almost 100% of the time.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 May 2020.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 3; 2024: 3; 2025: 1.

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