wsGAT: Weighted and Signed Graph Attention
A graph attention layer for networks with weighted and signed links, from the 2022 paper on link prediction.
About the resource
This repository accompanies the paper:
M. Grassia and G. Mangioni. wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction. Complex Networks & Their Applications X (2022).
wsGAT extends the Graph Attention Network (GAT) layer to graphs whose links carry a weight and a sign, such as trust and correlation networks. The paper uses it to predict the weight, the sign and the existence of links.
Use the resource
The repository contains the layer, wsGATConv, written for PyTorch
Geometric.
Citation and licence
Please cite the paper. The code is released under GPLv3.