  Graph Neural Networks (GNNs) have been widely used to learn representations
on graphs and tackle many real-world problems from a wide range of domains. In
this paper we propose wsGAT, an extension of the Graph Attention Network (GAT)
layers, meant to address the lack of GNNs that can handle graphs with signed
and weighted links, which are ubiquitous, for instance, in trust and
correlation networks. We first evaluate the performance of our proposal by
comparing against GCNII in the weighed link prediction task, and against SGCN
in the link sign prediction task. After that, we combine the two tasks and show
their performance on predicting the signed weight of links, and their
existence. Our results on real-world networks show that models with wsGAT
layers outperform the ones with GCNII and SGCN layers, and that there is no
loss in performance when signed weights are predicted.
