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].
Extrapolating Paths with Graph Neural Networks
Authors: Jean-Baptiste Cordonnier, Andreas Loukas
IJCAI 2019 | Venue PDF | LLM Run Details
| 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. |