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 | Conference PDF | Archive PDF | Plain Text | 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. |