Neural Bipartite Matching

• URL: http://arxiv.org/abs/2005.11304v4
• Date: Thu, 11 Jul 2024 15:06:35 GMT
• Title: Neural Bipartite Matching
• Authors: Dobrik Georgiev, Pietro Liò,
• Abstract summary: This report describes how neural execution is applied to a complex algorithm. It 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.
• Score: 19.600193617583955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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