PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs

• URL: http://arxiv.org/abs/2202.10793v5
• Date: Tue, 21 Nov 2023 18:51:56 GMT
• Title: PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs
• Authors: Yixuan He, Xitong Zhang, Junjie Huang, Benedek Rozemberczki, Mihai Cucuringu, Gesine Reinert
• Abstract summary: PyTorch Geometric Signed Directed (PyGSD) is a software package for signed and directed networks. PyGSD consists of easy-to-use GNN models, synthetic and real-world data, as well as task-specific evaluation metrics and loss functions. 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.
• Score: 20.832917829426098
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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