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..
Principal Neighbourhood Aggregation for Graph Nets
Authors: Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Veličković
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we compare the capacity of different models to capture and exploit the graph structure via a novel benchmark containing multiple tasks taken from classical graph theory, alongside existing benchmarks from realworld domains, all of which demonstrate the strength of our model. |
| Researcher Affiliation | Collaboration | Gabriele Corso University of Cambridge EMAIL Luca Cavalleri University of Cambridge EMAIL Dominique Beaini In Vivo AI EMAIL Pietro Liò University of Cambridge EMAIL Petar Veliˇckovi c Deep Mind EMAIL |
| Pseudocode | No | The paper describes methods using mathematical equations and descriptions, but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | The code for all the aggregators, scalers, models (in Py Torch, DGL and Py Torch Geometric frameworks), architectures, multi-task dataset generation and real-world benchmarks is available here. |
| Open Datasets | Yes | To further demonstrate the performance of our model, we also run tests on recently proposed realworld GNN benchmark datasets [5, 22] with tasks taken from molecular chemistry and computer vision. |
| Dataset Splits | Yes | Learning rates, weight decay, dropout and other hyperparameters were tuned on the validation set. |
| Hardware Specification | No | The paper does not specify the exact hardware components (e.g., specific GPU or CPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions software frameworks like 'Py Torch, DGL and Py Torch Geometric frameworks' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | We trained the models using the Adam optimizer for a maximum of 10,000 epochs, using early stopping with a patience of 1,000 epochs. Learning rates, weight decay, dropout and other hyperparameters were tuned on the validation set. |