Some Might Say All You Need Is Sum
Authors: Eran Rosenbluth, Jan Tönshoff, Martin Grohe
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, in Section 7 we experiment with synthetic data and observe that what we proved to be expressible is to an extent also learnable, and that in practice inexpressivity is manifested in a significantly higher error than implied in theory. All proofs, some of the lemmas, and extended illustration and analysis of the experimentation, are found in the full version1. |
| Researcher Affiliation | Academia | Eran Rosenbluth , Jan Toenshoff and Martin Grohe RWTH Aachen University {rosenbluth, toenshoff, grohe}@informatik.rwth-aachen.de |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | code for running the experiments is found at https://github.com/ toenshoff/Uniform Graph Learning |
| Open Datasets | No | This dataset consists of the star graphs {Gk,c} from Section 5.1, for k, c [1..1000]... This dataset consists of the graphs {Gk,c} from Section 5.2, for k, c [1..1000]. As training data, we vary k [1..100] and c [1..100]. We therefore train on 10K graphs in each experiment. |
| Dataset Splits | No | The paper mentions training data and test data, but does not explicitly specify a separate validation split or its details. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies used in the experiments. |
| Experiment Setup | No | Specific details concerning training and architecture, as well additional illustrations and extended analysis, can be found in the full version. The main paper mentions a "GNN architecture consisting of two GNN layers" but lacks concrete hyperparameters or training configurations. |