Awesome-Mirrors/awesome-network-analysis
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close #105 - thanks @tfardet

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François Briatte 2023-04-26 17:05:29 +02:00
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@ -642,6 +642,7 @@ __Note:__ searching for ‘@’ will return all Twitter accounts listed on this
- [networkx](http://networkx.github.io/) - Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. - [networkx](http://networkx.github.io/) - Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
- [Implementing an ERGM from Scratch in Python](https://gist.github.com/dmasad/8509304), using networkx and numpy (2014). - [Implementing an ERGM from Scratch in Python](https://gist.github.com/dmasad/8509304), using networkx and numpy (2014).
- [nxviz](https://github.com/ericmjl/nxviz/) - Visualization package for NetworkX. - [nxviz](https://github.com/ericmjl/nxviz/) - Visualization package for NetworkX.
- [nngt](https://nngt.readthedocs.io) - Library-agnostic graph generation and analysis that wraps around `networkx`, `igraph` and `graph-tool`). Includes normalized graph measures, advanced visualizations, (geo)spatial tools, and interfaces for neuroscience simulators.
- [npartite](https://github.com/ike002jp/npartite) - Python algorithms for community detection in n-partite networks. - [npartite](https://github.com/ike002jp/npartite) - Python algorithms for community detection in n-partite networks.
- [PyGraphistry](https://github.com/graphistry/pygraphistry) - Python library to extract, transform, and visually explore big graphs. - [PyGraphistry](https://github.com/graphistry/pygraphistry) - Python library to extract, transform, and visually explore big graphs.
- [python-igraph](http://igraph.org/python/) - Python version of the igraph network analysis package. - [python-igraph](http://igraph.org/python/) - Python version of the igraph network analysis package.
@ -967,8 +968,9 @@ Alden S. Klovdahl,
[Benedek Rozemberczki](https://github.com/benedekrozemberczki), [Benedek Rozemberczki](https://github.com/benedekrozemberczki),
[Alex Loftus](https://github.com/loftusa), [Alex Loftus](https://github.com/loftusa),
[Arun](https://github.com/arunppsg), [Arun](https://github.com/arunppsg),
[Filippo Menczer](https://cnets.indiana.edu/fil/) and [Filippo Menczer](https://cnets.indiana.edu/fil/),
[Marc Schiller](https://github.com/m4rcs) – [Marc Schiller](https://github.com/m4rcs) and
[Tanguy Fardet](https://tfardet.srht.site/) -
have waived all copyright and related or neighboring rights to this work. have waived all copyright and related or neighboring rights to this work.
Thanks to [Robert J. Ackland](https://github.com/rjackland), Thanks to [Robert J. Ackland](https://github.com/rjackland),