1 code implementation • 30 Oct 2023 • Beatrice Bevilacqua, Moshe Eliasof, Eli Meirom, Bruno Ribeiro, Haggai Maron
Subgraph GNNs are provably expressive neural architectures that learn graph representations from sets of subgraphs.
no code implementations • 17 Oct 2020 • Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, Haggai Maron
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.
no code implementations • 28 Sep 2020 • Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, Haggai Maron
We further demonstrate on several tasks, that training GNNs on small graphs results in solutions which do not generalize to larger graphs.