4 citations · 7 across the 8 of their papers we have counts for
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cs.LG2023★ 2 cited
Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems
Chendi Qian, Didier Chételat, Christopher Morris
Recently, machine learning, particularly message-passing graph neural networks (MPNNs), has gained traction in enhancing exact optimization algorithms. For example, MPNNs speed up…
cs.LG2023★ 4 cited
Probabilistically Rewired Message-Passing Neural Networks
Chendi Qian, Andrei Manolache, Kareem Ahmed +4
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input. However, they operate on a fixed input graph structure, ignoring pote…