(Architecture of a naive GCN versus that of PE-GNN, enhanced with a positional encoder.)
This is the official repository for the AISTATS 2023 paper Positional Encoder Graph Neural Networks for Geographic Data (Konstantin Klemmer, Nathan Safir, Daniel B. Neill).
The source code for PE-GNN (using PyTorch) can be found in the src folder. Its built on PyTorch Geometric (ICLR-W, 2019) and Space2Vec (ICLR, 2020).
We also provide an interactive example notebook to test PE-GNN via Google Colab
If you want to cite our work, you can use the following reference:
@InProceedings{pmlr-v206-klemmer23a,
title = {Positional Encoder Graph Neural Networks for Geographic Data},
author = {Klemmer, Konstantin and Safir, Nathan S. and Neill, Daniel B.},
booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
pages = {1379--1389},
year = {2023},
editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
volume = {206},
series = {Proceedings of Machine Learning Research},
month = {25--27 Apr},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v206/klemmer23a/klemmer23a.pdf},
url = {https://proceedings.mlr.press/v206/klemmer23a.html},
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent