pygrank: A Python Package for Graph Node Ranking
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Abstract (arXiv)
We introduce pygrank, an open source Python package to define, run and evaluate node ranking algorithms. We provide object-oriented and extensively unit-tested algorithm components, such as graph filters, post-processors, measures, benchmarks and online tuning. Computations can be delegated to numpy, tensorflow or pytorch backends and fit in back-propagation pipelines. Classes can be combined to define interoperable complex algorithms. Within the context of this paper we compare the package with related alternatives and demonstrate its flexibility and ease of use with code examples.
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References · 10
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
- 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
- 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 413
- 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 383
- 2019Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networkscited by 242
- 2020Combining Label Propagation and Simple Models Out-performs Graph Neural Networkscited by 202
- 2020Graph Neural Networks in TensorFlow and Keras with Spektralcited by 60
- 2016The ground truth about metadata and community detection in networkscited by 42
- 2019Pairwise Link Predictioncited by 4
- 2021Prior Signal Editing for Graph Filter Posterior Fairness Constraintscited by 1
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