Graph Neural Networks Use Graphs When They Shouldn't
Fuente:
arXiv
Saved in:
| Main Authors: | Bechler-Speicher, Maya, Amos, Ido, Gilad-Bachrach, Ran, Globerson, Amir |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The Interpretable and Effective Graph Neural Additive Networks
by: Bechler-Speicher, Maya, et al.
Published: (2024)
by: Bechler-Speicher, Maya, et al.
Published: (2024)
TREE-G: Decision Trees Contesting Graph Neural Networks
by: Bechler-Speicher, Maya, et al.
Published: (2022)
by: Bechler-Speicher, Maya, et al.
Published: (2022)
On the Utilization of Unique Node Identifiers in Graph Neural Networks
by: Bechler-Speicher, Maya, et al.
Published: (2024)
by: Bechler-Speicher, Maya, et al.
Published: (2024)
Depth-Width tradeoffs in Algorithmic Reasoning of Graph Tasks with Transformers
by: Yehudai, Gilad, et al.
Published: (2025)
by: Yehudai, Gilad, et al.
Published: (2025)
Towards Invariance to Node Identifiers in Graph Neural Networks
by: Bechler-Speicher, Maya, et al.
Published: (2025)
by: Bechler-Speicher, Maya, et al.
Published: (2025)
Graph Mixing Additive Networks
by: Bechler-Speicher, Maya, et al.
Published: (2025)
by: Bechler-Speicher, Maya, et al.
Published: (2025)
Cayley Graph Propagation
by: Wilson, JJ, et al.
Published: (2024)
by: Wilson, JJ, et al.
Published: (2024)
A Graph Meta-Network for Learning on Kolmogorov-Arnold Networks
by: Bar-Shalom, Guy, et al.
Published: (2026)
by: Bar-Shalom, Guy, et al.
Published: (2026)
Lost in Tokenization: Fundamental Trade-offs in Graph Tokenization for Transformers
by: Bechler-Speicher, Maya, et al.
Published: (2026)
by: Bechler-Speicher, Maya, et al.
Published: (2026)
Billion-Scale Graph Foundation Models
by: Bechler-Speicher, Maya, et al.
Published: (2026)
by: Bechler-Speicher, Maya, et al.
Published: (2026)
A General Recipe for Contractive Graph Neural Networks -- Technical Report
by: Bechler-Speicher, Maya, et al.
Published: (2024)
by: Bechler-Speicher, Maya, et al.
Published: (2024)
Open Shouldn't Mean Exempt: Open-Source Exceptionalism and Generative AI
by: Atkinson, David
Published: (2025)
by: Atkinson, David
Published: (2025)
Spectral Graph Neural Networks are Incomplete on Graphs with a Simple Spectrum
by: Hordan, Snir, et al.
Published: (2025)
by: Hordan, Snir, et al.
Published: (2025)
When Can Transformers Count to n?
by: Yehudai, Gilad, et al.
Published: (2024)
by: Yehudai, Gilad, et al.
Published: (2024)
SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data
by: Bechler-Speicher, Maya, et al.
Published: (2025)
by: Bechler-Speicher, Maya, et al.
Published: (2025)
Check Your LLM's Secret Dictionary! Five Lines of Code Reveal What Your LLM Learned (Including What It Shouldn't Have)
by: Miyashita, Hisashi
Published: (2026)
by: Miyashita, Hisashi
Published: (2026)
Content Augmented Graph Neural Networks
by: Nasrabadi, Fatemeh Gholamzadeh, et al.
Published: (2023)
by: Nasrabadi, Fatemeh Gholamzadeh, et al.
Published: (2023)
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks
by: Bechler-Speicher, Maya, et al.
Published: (2025)
by: Bechler-Speicher, Maya, et al.
Published: (2025)
Graphs Unveiled: Graph Neural Networks and Graph Generation
by: Kovács, László, et al.
Published: (2024)
by: Kovács, László, et al.
Published: (2024)
FSW-GNN: A Bi-Lipschitz WL-Equivalent Graph Neural Network
by: Sverdlov, Yonatan, et al.
Published: (2024)
by: Sverdlov, Yonatan, et al.
Published: (2024)
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
by: Jiang, Wenzhao, et al.
Published: (2023)
by: Jiang, Wenzhao, et al.
Published: (2023)
GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
by: Fu, Xingbo, et al.
Published: (2025)
by: Fu, Xingbo, et al.
Published: (2025)
GraphXAIN: Narratives to Explain Graph Neural Networks
by: Cedro, Mateusz, et al.
Published: (2024)
by: Cedro, Mateusz, et al.
Published: (2024)
Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
by: Fan, Shaohua, et al.
Published: (2021)
by: Fan, Shaohua, et al.
Published: (2021)
Explaining Graph Neural Networks for Node Similarity on Graphs
by: Daza, Daniel, et al.
Published: (2024)
by: Daza, Daniel, et al.
Published: (2024)
Cooperative Graph Neural Networks
by: Finkelshtein, Ben, et al.
Published: (2023)
by: Finkelshtein, Ben, et al.
Published: (2023)
Commute Graph Neural Networks
by: Zhuo, Wei, et al.
Published: (2024)
by: Zhuo, Wei, et al.
Published: (2024)
Superposition in Graph Neural Networks
by: Pertl, Lukas, et al.
Published: (2025)
by: Pertl, Lukas, et al.
Published: (2025)
Transformers are Graph Neural Networks
by: Joshi, Chaitanya K.
Published: (2025)
by: Joshi, Chaitanya K.
Published: (2025)
Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural Networks
by: Liu, Xuyuan, et al.
Published: (2024)
by: Liu, Xuyuan, et al.
Published: (2024)
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
by: Younesian, Taraneh, et al.
Published: (2023)
by: Younesian, Taraneh, et al.
Published: (2023)
When Should LLMs Be Less Specific? Selective Abstraction for Reliable Long-Form Text Generation
by: Goren, Shani, et al.
Published: (2026)
by: Goren, Shani, et al.
Published: (2026)
Graph Unlearning: Efficient Node Removal in Graph Neural Networks
by: Guan, Faqian, et al.
Published: (2025)
by: Guan, Faqian, et al.
Published: (2025)
Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphs
by: Sasaki, Yuya
Published: (2025)
by: Sasaki, Yuya
Published: (2025)
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
by: Bei, Yuanchen, et al.
Published: (2024)
by: Bei, Yuanchen, et al.
Published: (2024)
Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks
by: Attali, Hugo, et al.
Published: (2025)
by: Attali, Hugo, et al.
Published: (2025)
Learning to Execute Graph Algorithms Exactly with Graph Neural Networks
by: Qharabagh, Muhammad Fetrat, et al.
Published: (2026)
by: Qharabagh, Muhammad Fetrat, et al.
Published: (2026)
GraphBench: Next-generation graph learning benchmarking
by: Stoll, Timo, et al.
Published: (2025)
by: Stoll, Timo, et al.
Published: (2025)
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
by: Kofinas, Miltiadis, et al.
Published: (2024)
by: Kofinas, Miltiadis, et al.
Published: (2024)
Kolmogorov-Arnold Graph Neural Networks
by: De Carlo, Gianluca, et al.
Published: (2024)
by: De Carlo, Gianluca, et al.
Published: (2024)
Similar Items
-
The Interpretable and Effective Graph Neural Additive Networks
by: Bechler-Speicher, Maya, et al.
Published: (2024) -
TREE-G: Decision Trees Contesting Graph Neural Networks
by: Bechler-Speicher, Maya, et al.
Published: (2022) -
On the Utilization of Unique Node Identifiers in Graph Neural Networks
by: Bechler-Speicher, Maya, et al.
Published: (2024) -
Depth-Width tradeoffs in Algorithmic Reasoning of Graph Tasks with Transformers
by: Yehudai, Gilad, et al.
Published: (2025) -
Towards Invariance to Node Identifiers in Graph Neural Networks
by: Bechler-Speicher, Maya, et al.
Published: (2025)