Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Position: Future Directions in the Theory of Graph Machine Learning
Authors: Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, Stefanie Jegelka
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this position paper, we argue that the graph machine learning community needs to shift its attention to developing a balanced theory of graph machine learning, focusing on a more thorough understanding of the interplay of expressive power, generalization, and optimization. |
| Researcher Affiliation | Collaboration | 1RWTH Aachen University 2Technion Israel Institute of Technology 3NVIDIA Research 4University of Oxford 5MIT 6TU Munich. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions existing open-source libraries (PYTORCH GEOMETRIC, DGL) and refers to code released by other authors ('Wang & Zhang (2023a) took a first step in this direction by providing open-source implementations'), but it does not provide concrete access to source code for the concepts or analyses described in this position paper. |
| Open Datasets | No | This is a position paper that does not conduct experiments, therefore it does not provide access information for a dataset used for training. It mentions datasets like BREC as examples from other work but does not use them itself. |
| Dataset Splits | No | This is a position paper that does not conduct experiments, therefore it does not provide specific dataset split information for validation. |
| Hardware Specification | No | This is a position paper that does not conduct experiments and therefore does not provide specific hardware details. |
| Software Dependencies | No | This is a position paper that does not conduct experiments and therefore does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | This is a position paper that does not conduct experiments and therefore does not provide specific experimental setup details. |