Position-aware Graph Neural Networks
Authors: Jiaxuan You, Rex Ying, Jure Leskovec
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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. |