Single-Node Attack for Fooling Graph Neural Networks. - Ben Finkelshtein; Chaim Baskin; Evgenii Zheltonozhskii; Uri Alon 002 (CoRR, 2020)

A Simple and Universal Rotation Equivariant Point-Cloud Network. - Ben Finkelshtein; Chaim Baskin; Haggai Maron; Nadav Dym (TAG-ML, 2022)

Single-node attacks for fooling graph neural networks. - Ben Finkelshtein; Chaim Baskin; Evgenii Zheltonozhskii; Uri Alon 002 (Neurocomputing, 2022)

Strategic Classification with Graph Neural Networks. - Itay Eilat; Ben Finkelshtein; Chaim Baskin; Nir Rosenfeld (ICLR, 2023)

Cooperative Graph Neural Networks. - Ben Finkelshtein; Xingyue Huang; Michael M. Bronstein; Ismail Ilkan Ceylan (ICML, 2024)

Almost Surely Asymptotically Constant Graph Neural Networks. - Sam Adam-Day; Michael Benedikt; Ismail Ilkan Ceylan; Ben Finkelshtein (NeurIPS, 2024)

Learning on Large Graphs using Intersecting Communities. - Ben Finkelshtein; Ismail Ilkan Ceylan; Michael M. Bronstein; Ron Levie (NeurIPS, 2024)

Graph neural network outputs are almost surely asymptotically constant. - Sam Adam-Day; Michael Benedikt; Ismail Ilkan Ceylan; Ben Finkelshtein (CoRR, 2024)

Covered Forest: Fine-grained generalization analysis of graph neural networks. - Antonis Vasileiou; Ben Finkelshtein; Floris Geerts; Ron Levie; Christopher Morris 001 (CoRR, 2024)

Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks. - Maya Bechler-Speicher; Ben Finkelshtein; Fabrizio Frasca; Luis Müller; Jan Tönshoff; Antoine Siraudin; Viktor Zaverkin; Michael M. Bronstein; Mathias Niepert; Bryan Perozzi; Mikhail Galkin 001; Christopher Morris 001 (CoRR, 2025)

Efficient Learning on Large Graphs using a Densifying Regularity Lemma. - Jonathan Kouchly; Ben Finkelshtein; Michael M. Bronstein; Ron Levie (CoRR, 2025)

Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models. - Ben Finkelshtein; Ismail Ilkan Ceylan; Michael M. Bronstein; Ron Levie (CoRR, 2025)