  Networks are ubiquitous in many real-world applications (e.g., social
networks encoding trust/distrust relationships, correlation networks arising
from time series data). While many networks are signed or directed, or both,
there is a lack of unified software packages on graph neural networks (GNNs)
specially designed for signed and directed networks. In this paper, we present
PyTorch Geometric Signed Directed (PyGSD), a software package which fills this
gap. Along the way, we evaluate the implemented methods with experiments with a
view to providing insights into which method to choose for a given task. The
deep learning framework consists of easy-to-use GNN models, synthetic and
real-world data, as well as task-specific evaluation metrics and loss functions
for signed and directed networks. As an extension library for PyG, our proposed
software is maintained with open-source releases, detailed documentation,
continuous integration, unit tests and code coverage checks. The GitHub
repository of the library is
https://github.com/SherylHYX/pytorch_geometric_signed_directed.

Machine Learning;Artificial Intelligence;Social and Information Networks;Machine Learning