References

This package interacts with or is built on top of the following libraries:

• Deep Graph Library [1]

• DyNetX [2]

• graph-tool [3]

• igraph [4]

• matplotlib [5]

• NetworKit [6]

• NetworkX [7]

• Pandas [8]

• PyTorch Geometric [9]

• Stanford Network Analysis Platform [10]

• StellarGraph [11]

• Teneto [12]

For more information on specific packages, please refer to their respective documentation.

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[1]

Minjie Wang et al. (2019). ‘’Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks’’. doi: 10.48550/arXiv.1909.01315

[2]

Giulio Rossetti (2020). ‘’DyNetx: dynamic network analysis library.’’ doi: 10.5281/zenodo.3953118

[3]

Tiago P. Peixoto (2014). ‘’The graph-tool python library’’. doi: 10.6084/m9.figshare.1164194

[4]

Gábor Csárdi, Tamas Nepusz (2006). ‘’The igraph software package for complex network research’’. InterJournal, Complex Systems, 1695. doi: 10.5281/zenodo.3630268

[5]

John D. Hunter (2007). ‘’Matplotlib: A 2D Graphics Environment’’. Computing in Science & Engineering, 9(3), pp. 90-95. doi: 10.1109/MCSE.2007.55

[6]

Christian L. Staudt, Aleksejs Sazonovs, Henning Meyerhenke. (2016). ‘’NetworKit: A Tool Suite for Large-scale Complex Network Analysis’’. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1967-1970. doi: 10.48550/arXiv.1403.3005

[7]

Aric A. Hagberg, Daniel A. Schult, Pieter J. Swart (2008). ‘’Exploring network structure, dynamics, and function using NetworkX’’. In Proceedings of the 7th Python in Science Conference (SciPy2008), pp. 11-15. doi: 10.25080/TCWV9851

[8]

Wes McKinney (2010). ‘’Data Structures for Statistical Computing in Python’’. In Proceedings of the 9th Python in Science Conference, 445. doi: 10.25080/Majora-92bf1922-00a

[9]

Matthias Fey, Jan E. Lenssen, F. Weichert (2019). ‘’Fast Graph Representation Learning with PyTorch Geometric’’. In ICLR Workshop on Representation Learning on Graphs, Manifolds. doi: 10.48550/arXiv.1903.02428

[10]

Jure C. Leskovec, Rok Sosič (2016). ‘’SNAP: A General-Purpose Network Analysis, Graph-Mining Library’’. In ACM Transactions on Intelligent Systems, Technology (TIST), 8(1), 1, pp. 1-20. doi: 10.1145/2898361

[11]

CSIRO’s Data61 (2018). ‘’StellarGraph Machine Learning Library.’’ GitHub repository. https://github.com/stellargraph/stellargraph

[12]

William H. Thompson, Per Brantefors, Peter Fransson (2017). ‘’From static to temporal network theory applications to functional brain connectivity’’. Network Neuroscience, 2(1), pp. 69-99. doi: 10.1162/NETN_a_00011