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Found 3 papers, 1 papers with code
Efficient Subgraph GNNs by Learning Effective Selection Policies
Subgraph GNNs are provably expressive neural architectures that learn graph representations from sets of subgraphs.
From Local Structures to Size Generalization in Graph Neural Networks
In this paper, we identify an important type of data where generalization from small to large graphs is challenging: graph distributions for which the local structure depends on the graph size.
On Size Generalization in Graph Neural Networks
We further demonstrate on several tasks, that training GNNs on small graphs results in solutions which do not generalize to larger graphs.
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