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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Position-aware Graph Neural Networks
Authors: Jiaxuan You, Rex Ying, Jure Leskovec
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply P-GNNs to 8 different datasets and several different prediction tasks including link prediction and community detection1. In all datasets and prediction tasks, we show that P-GNNs consistently outperforms state of the art GNN variants, with up to 66% AUC score improvement. (Section 6: Experiments, Table 1 and Table 2 present numerical results from these experiments) |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stanford University, Stanford, CA, USA. |
| Pseudocode | Yes | Algorithm 1 The framework of P-GNNs |
| Open Source Code | Yes | Code and data are available in http://snap.stanford.edu/pgnn/ |
| Open Datasets | Yes | PPI. 24 Protein-protein interaction networks (Zitnik & Leskovec, 2017). Emails. 7 real-world email communication graphs from SNAP (Leskovec et al., 2007) |
| Dataset Splits | Yes | Specifically, we follow the experimental setting from (Zhang & Chen, 2018), and use two sets of 10% existing links and an equal number of nonexistent links as test and validation sets, with the remaining 80% existing links and equal number of nonexistent links used as the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions that 'Implementational details are provide in the Appendix.' but does not include specific experimental setup details like hyperparameter values in the main text. |