Awesome

EGNNA_WND

Pytorch code for estimating the presence of the West Nile Disease employing Graph Neural network. I use the technique proposed in the paper: <a href="https://openaccess.thecvf.com/content_CVPR_2019/papers/Gong_Exploiting_Edge_Features_for_Graph_Neural_Networks_CVPR_2019_paper.pdf">Exploiting Edge Features for Graph Neural Networks</a>. With these method I am able to exploit not only the node features but also the edge ones, in detail I adopt three different similarity matrices calculated from the temperature (LST), the soil moisture (SSM) and the altitude (SRTM) values.

Model architecture

Graph aggregation on embedding features

Prerequisites

Python >= 3.7
PyTorch >= 1.5
CUDA 10.0

Dataset

A Custom Dataset provided by European Space Agency (ESA) is adopted, at the moment I'm not allowed to publish it. I will try to insert a fake version as soon as possible.

Models

ResNet18 is used as feature extractor, followed by a graph aggregation and a final linear layer. Both the versions based on Graph Attention Network and Graph Convolutional Network are tested.

Training

Before running the files main.py you can set the desired parameters in the file job_config.py, which modify the ones contained in config/configuration.json.

Cite

If you have any questions, please contact stefano.vincenzi@unimore.it, or open an issue on this repo.