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PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs (2202.10793v6)

Published 22 Feb 2022 in cs.LG, cs.AI, cs.SI, and stat.ML

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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Authors (6)
  1. Yixuan He (20 papers)
  2. Xitong Zhang (20 papers)
  3. Junjie Huang (73 papers)
  4. Benedek Rozemberczki (23 papers)
  5. Mihai Cucuringu (76 papers)
  6. Gesine Reinert (70 papers)
Citations (10)
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