arXiv Machine Learning
Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputation
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CoRe-GNN: Multilevel Message passing on Coarsened graphs
arXiv:2608. 02128v1 Announce Type: new Abstract: Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers.
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CoRe-GNN: Multilevel Message passing on Coarsened graphs
Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches can be written as structured modifications of the GNN propagation matrix, providing a unified perspective that exposes their respective limitations.
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Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks
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Plain Transformers are Surprisingly Powerful Link Predictors
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arXiv Machine Learning
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