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size-transferability

topic1 events
papersTODAY 04:00 UTC

Paper Studies Size Transferability of Graph Transformers with Convolutional Positional Encodings

A new arXiv paper investigates how well Graph Transformers generalize when trained on graphs of one size and evaluated on larger or smaller ones. It focuses on positional encodings derived from graph neural networks, a common design element in these attention-based models for graph data. The work examines whether such encodings help preserve performance as graph size changes.

graph-transformers/research/transferability/gnn