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 [1].
Effective Structural Encodings via Local Curvature Profiles
Authors: Lukas Fesser, Melanie Weber
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyze the effectiveness of LCP through a range of experiments, which reveal LCP s superior performance in nodeand graph-level tasks. |
| Researcher Affiliation | Academia | Lukas Fesser Faculty of Arts and Sciences Harvard University lukas EMAIL Melanie Weber Department of Applied Mathematics Harvard University EMAIL |
| Pseudocode | No | The paper describes methods and calculations (e.g., LCP definition, ORC computation) in narrative text and mathematical formulas but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/Weber-GeoML/Local_Curvature_Profile |
| Open Datasets | Yes | We conduct our node classification experiments on the publicly available CORA and CITESEER Yang et al. (2016) datasets, and our graph classification experiments on the ENZYMES, IMDB, MUTAG and PROTEINS datasets from the TUDataset collection Morris et al. (2020). |
| Dataset Splits | Yes | We record the test set accuracy of the settings with the highest validation accuracy. ... We use a train/val/test split of 50/25/25. |
| Hardware Specification | Yes | Our experiments were conducted on a local server with the specifications presented in the following table. COMPONENTS SPECIFICATIONS ARCHITECTURE X86 64 OS UBUNTU 20.04.5 LTS x86 64 CPU AMD EPYC 7742 64-CORE GPU NVIDIA A100 TENSOR CORE RAM 40GB |
| Software Dependencies | No | The paper states: "We implemented all experiments in this paper in Python using PyTorch, Numpy PyTorch Geometric, and Python Optimal Transport." However, specific version numbers for these libraries are not provided. |
| Experiment Setup | Yes | Node classification. We use a GNN with 3 layers and hidden dimension 128. We further use a dropout probability of 0.5, and a ReLU activation. ... Graph classification. We use a GNN with 4 layers and hidden dimension 64. We further use a dropout probability of 0.5, and a ReLU activation. ... Unless explicitly stated otherwise, we train all models until we observe no improvements in the validation accuracy for 100 epochs using the Adam optimizer with learning rate 1e-3 and a batch size of 16. |