36 citations · 90 across the 6 of their papers we have counts for
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cs.LG2021★ 11 cited
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings
Matthias Fey, Jan E. Lenssen, Frank Weichert +1
We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs to large graphs. GAS prunes entire sub-trees of the computation graph by utilizing historical…
cs.LG2020★ 36 cited
Hierarchical Inter-Message Passing for Learning on Molecular Graphs
Matthias Fey, Jan-Gin Yuen, Frank Weichert
We present a hierarchical neural message passing architecture for learning on molecular graphs. Our model takes in two complementary graph representations: the raw molecular graph…