论文标题
Pytorch几何签名指示：图形神经网络上的软件包，用于签名和定向图
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs
论文作者
论文摘要
网络在许多现实世界应用程序中无处不在（例如，编码信任/不信任关系的社交网络，由时间序列数据引起的相关网络）。尽管许多网络都是签名或指示的，或者两者兼而有之，但在图形神经网络（GNN）上缺少统一的软件包，专门为签名和有导网络设计。在本文中，我们提出了Pytorch几何签名（Pygsd），该软件包填充了此空白。一路上，我们通过实验评估实现的方法，以提供有关为给定任务选择哪种方法的见解。深度学习框架包括易于使用的GNN模型，合成和现实世界数据，以及针对签名和定向网络的特定任务评估指标和损失功能。作为PYG的扩展库，我们提出的软件由开源版本，详细文档，连续集成，单位测试和代码覆盖范围检查维护。库的github存储库是https://github.com/sherylhyx/pytorch_geometric_signed_directed。
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.
