Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Extrapolating Paths with Graph Neural Networks
Authors: Jean-Baptiste Cordonnier, Andreas Loukas
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,273 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,523 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments with GPS traces on a road network and user-navigation on Wikipedia con๏ฌrm that GRETEL can adapt to graphs with very different properties, while comparing favorably to previous solutions. |
| Researcher Affiliation | Academia | Jean-Baptiste Cordonnier and Andreas Loukas Ecole Polytechnique F ed erale de Lausanne {jean-baptiste.cordonnier, andreas.loukas}@ep๏ฌ.ch |
| Pseudocode | No | The paper describes algorithmic steps and mathematical formulations for GRETEL's components and operations but does not present them in clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Information about the datasets and hyper-parameters are displayed in Table 1, code and datasets are publicly available at https://github.com/jbcdnr/gretel-path-extrapolation. |
| Open Datasets | Yes | Information about the datasets and hyper-parameters are displayed in Table 1, code and datasets are publicly available at https://github.com/jbcdnr/gretel-path-extrapolation. ... We ran an experiment based on a small dataset of food deliveries (229 traces) occurring over the Open Street Map road network of Lausanne (18156 nodes, 32468 edges). ... In the Wikispeedia game [West et al., 2009]... |
| Dataset Splits | No | Table 1 lists 'train/test 80% / 20%' for both GPS and Wikispeedia datasets, but no explicit validation split percentage or methodology is mentioned. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions training parameters like 'Adam learning rate' but does not specify software dependencies (e.g., Python, PyTorch, TensorFlow versions) with specific version numbers. |
| Experiment Setup | Yes | Information about the datasets and hyper-parameters are displayed in Table 1: Adam learning rate 0.01 0.1 batch size 5 10 # epochs 200 5. |