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Maya Bechler-Speicher
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2020 – today
- 2026
[i16]Maya Bechler-Speicher, Yoel Gottlieb, Andrey Isakov, David Abensur, Ami Tavory, Daniel Haimovich, Ido Guy, Udi Weinsberg:
Billion-Scale Graph Foundation Models. CoRR abs/2602.04768 (2026)- 2025
[c5]Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca, Luis Müller, Jan Tönshoff, Antoine Siraudin, Viktor Zaverkin, Michael M. Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, Christopher Morris:
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks. ICML (Position Papers) 2025
[i15]Maya Bechler-Speicher, Moshe Eliasof, Carola-Bibiane Schönlieb, Ran Gilad-Bachrach, Amir Globerson:
Towards Invariance to Node Identifiers in Graph Neural Networks. CoRR abs/2502.13660 (2025)
[i14]Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca, Luis Müller, Jan Tönshoff, Antoine Siraudin, Viktor Zaverkin, Michael M. Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, Christopher Morris:
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks. CoRR abs/2502.14546 (2025)
[i13]Gilad Yehudai, Clayton Sanford, Maya Bechler-Speicher, Orr Fischer, Ran Gilad-Bachrach, Amir Globerson:
Depth-Width tradeoffs in Algorithmic Reasoning of Graph Tasks with Transformers. CoRR abs/2503.01805 (2025)
[i12]Andrea Zerio, Maya Bechler-Speicher, Tine Jess, Aleksejs Sazonovs:
Identifying Critical Phases for Disease Onset with Sparse Haematological Biomarkers. CoRR abs/2503.14561 (2025)
[i11]Andrea Zerio, Maya Bechler-Speicher, Maor Huri, Marie Vibeke Vestergaard, Ran Gilad-Bachrach, Tine Jess, Samir Bhatt, Aleksejs Sazonovs:
Interpretable Graph Learning Over Sets of Temporally-Sparse Data. CoRR abs/2505.19193 (2025)
[i10]Snir Hordan, Maya Bechler-Speicher, Gur Lifshitz, Nadav Dym:
Spectral Graph Neural Networks are Incomplete on Graphs with a Simple Spectrum. CoRR abs/2506.05530 (2025)
[i9]Maya Bechler-Speicher, Andrea Zerio, Maor Huri, Marie Vibeke Vestergaard, Ran Gilad-Bachrach, Tine Jess, Samir Bhatt, Aleksejs Sazonovs:
Graph Mixing Additive Networks. CoRR abs/2509.23923 (2025)
[i8]Timo Stoll, Chendi Qian, Ben Finkelshtein, Ali Parviz, Darius Weber, Fabrizio Frasca, Hadar Shavit, Antoine Siraudin, Arman Mielke, Marie Anastacio, Erik Müller, Maya Bechler-Speicher, Michael M. Bronstein, Mikhail Galkin, Holger H. Hoos, Mathias Niepert, Bryan Perozzi, Jan Tönshoff, Christopher Morris:
GraphBench: Next-generation graph learning benchmarking. CoRR abs/2512.04475 (2025)- 2024
[c4]Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
:
TREE-G: Decision Trees Contesting Graph Neural Networks. AAAI 2024: 11032-11042
[c3]Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir Globerson:
Graph Neural Networks Use Graphs When They Shouldn't. ICML 2024: 3284-3304
[c2]JJ Wilson, Maya Bechler-Speicher, Petar Velickovic:
Cayley Graph Propagation. LoG 2024: 8
[c1]Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach:
The Intelligible and Effective Graph Neural Additive Network. NeurIPS 2024
[i7]Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach:
The Intelligible and Effective Graph Neural Additive Networks. CoRR abs/2406.01317 (2024)
[i6]JJ Wilson, Maya Bechler-Speicher, Petar Velickovic:
Cayley Graph Propagation. CoRR abs/2410.03424 (2024)
[i5]Maya Bechler-Speicher, Moshe Eliasof:
A General Recipe for Contractive Graph Neural Networks - Technical Report. CoRR abs/2411.01717 (2024)
[i4]Maya Bechler-Speicher, Moshe Eliasof, Carola-Bibiane Schönlieb, Ran Gilad-Bachrach, Amir Globerson:
On the Utilization of Unique Node Identifiers in Graph Neural Networks. CoRR abs/2411.02271 (2024)- 2023
[i3]Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach
, Amir Globerson:
Graph Neural Networks Use Graphs When They Shouldn't. CoRR abs/2309.04332 (2023)- 2022
[i2]Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
:
Graph Trees with Attention. CoRR abs/2207.02760 (2022)
2010 – 2019
- 2019
[i1]Maya Bechler-Speicher:
A Crossing Lemma for Families of Jordan Curves with a Bounded Intersection Number. CoRR abs/1911.07287 (2019)
Coauthor Index

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last updated on 2026-03-28 22:43 CET by the dblp team
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