What is recorded here?
Used sparse gather/scatter kernels, a message-passing interface and block-diagonal batching of variable-size graphs to integrate geometric deep-learning methods with PyTorch.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Implements inherited graph/point-cloud operators rather than inventing GCN, GraphSAGE, GAT or their theory. Speed and memory comparisons concern chosen setups; general irregular-data support is not arbitrary geometric invariance.
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People and systems
Matthias Fey; Jan E. Lenssen
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
PyTorch Geometric: sparse message passing and graph mini-batching
Used sparse gather/scatter kernels, a message-passing interface and block-diagonal batching of variable-size graphs to integrate geometric deep-learning methods with PyTorch.
Scientific scope
Implements inherited graph/point-cloud operators rather than inventing GCN, GraphSAGE, GAT or their theory. Speed and memory comparisons concern chosen setups; general irregular-data support is not arbitrary geometric invariance.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Matthias Fey; Jan E. Lenssen
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.