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Found 2 papers, 2 papers with code
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings
We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs to large graphs.
Deep Graph Matching Consensus
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.
Ranked #12 on
Entity Alignment
on DBP15k zh-en
(using extra training data)
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