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.

翻译：网络在众多实际应用中无处不在（例如，编码信任/不信任关系的社交网络、时间序列数据产生的相关网络）。尽管许多网络具有符号性、有向性或两者兼有，但目前尚缺乏专门为符号和有向网络设计的统一图神经网络（GNN）软件包。本文提出了填补这一空白的软件包PyTorch Geometric Signed Directed（PyGSD）。在此过程中，我们通过实验评估了所实现的方法，旨在为特定任务的方法选择提供指导。该深度学习框架包含易于使用的GNN模型、合成与真实世界数据，以及针对符号和有向网络的任务特定评估指标与损失函数。作为PyG的扩展库，本软件以开源形式持续维护，提供详细文档、持续集成、单元测试及代码覆盖率检查。该库的GitHub仓库地址为https://github.com/SherylHYX/pytorch_geometric_signed_directed。