From Local Structures to Size Generalization in Graph Neural Networks
Gilad Yehudai 1 Ethan Fetaya 2 Eli Meirom 1 Gal Chechik 1 2 Haggai Maron 1
Abstract
Graph neural networks (GNNs) can process
graphs of different sizes, but their ability to gen-
eralize across sizes, specifically from small to
large graphs, is still not well understood. In
this paper, we identify an important type of data
where generalization from small to large graphs
is c ...


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