Principal Neighbourhood Aggregation for Graph Nets
Authors: Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Veličković
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 gc579@cam.ac.uk Luca Cavalleri University of Cambridge lc737@cam.ac.uk Dominique Beaini In Vivo AI dominique@invivoai.com Pietro Liò University of Cambridge pietro.lio@cst.cam.ac.uk Petar Veliˇckovi c Deep Mind petarv@google.com |
| 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. |