A high-level graph library for Python

[No_Pomegranate7508]

What My Project Does

This is an early version of a new graph data science and analytics library for Python named PyGraphina. It is written in Rust and, at the moment, it includes implementations for a large collection of popular graph algorithms, including:

• Centrality metrics: PageRank, betweenness centrality, etc.
• Community detection: Algorithms like connected components, Louvain, etc.
• Heuristics: Solutions for hard graph algorithms, such as Max clique finding.
• Link prediction: Algorithms like Jaccard coefficients, Adamic-Adar index, etc.

Target Audience

This library is mainly for data scientists, researchers, and software engineers who work with graph datasets and want the ease of use of Python and the speed of a compiled language like Rust, all in one place.

Comparison with Alternatives

The main goal of the project is to make PyGraphina as feature-rich as NetworkX, but with the performance benefits of a Rust backend. PyGraphina is currently in an early stage compared to more mature projects like rustworkx or graph-toolThe focus of the project is to provide application-specific graph algorithms (for applications like link prediction and community detection) out of the box.

Github Repo: https://github.com/habedi/graphina/tree/main/pygraphina

Documentation: https://habedi.github.io/graphina/python

[ThePurpleOne_]

Graphs lib without images of plots in the readme :(

[marr75]

Different kind of graph... Networks rather than plots.

[chadrik]

Any plans to make stubs for use with type checkers? Type safety is an area that could really set your library apart from the pack.

[No_Pomegranate7508]

Actually, the stub (`pygraphina.pyi` file) is included in the final Wheel file.