A reliable idiomatic wrapper for networkx [$400]

[Krastanov]

A reliable idiomatic networkx wrapper [$400]

Using networkx through PythonCall.jl is trivially easy. This bounty is mostly about creating a new NetworkX.jl wrapper (by using PythonCall.jl) that provides quality-of-life improvements,

• Fast conversion and/or views between Graphs.jl types and the networkx structures
• Complete Graph API support for the newly created networkx wrapper types (so that networkx types can be used in all already existing Graph.jl algorithms that do not peek behind the API)
• use GraphsInterfaceChecker.jl in the test suite
• Dispatch from operations defined in Graphs/GraphsMatching/GraphsOptim to networkx implementation. For instance if there is a pre-existing Graphs.some_interesting_property(::AbstractGraph) there should now be a new method defined in NetworkX some_interesting_property(g::AbstractGraph, ::NXAlgorithm) that dispatches to the networkx implementation. It should convert the g argument to networkx graphs if necessary.
• If there is an algorithm defined in networkx that does not exist yet in Graphs.jl, it should be declared in Graphs.jl (just a function with docs but no methods), together with an error hint that NetworkX is necessary. Given the large scope of the library, this can be more of a proof-of-concept setup, documenting how to add everything, but not expecting everything to be added for completing the bounty.
• Proper tests and documentation.
• PRs on this topic have to be submitted with clean git histories and well compartmentalized for ease of review.

Required skills: familiarity with the Graphs.jl API and understanding of the PythonCall package

Reviewer: any Graphs.jl member with merge rights

Duration: 3 months

Payout procedure:

The Funding for these bounties comes from the National Science Foundation and from the NSF Center for Quantum Networks. The payouts are managed by the NumFOCUS foundation and processed in bulk once every two months. If you live in a country in which NumFOCUS can make payments, you can participate in this bounty program.

Click here for more details about the bug bounty program.

Bug bounty logistic details (click to expand)

To claim exclusive time to work on this bounty either post a comment here or message skrastanov@umass.edu with:

• your name
• github username
• (optional) a brief list of previous pertinent projects you have engaged in

If you want to, you can work on this project without making a claim, however claims are encouraged to give you and other contributors peace of mind. Whoever has made a claim takes precedence when solutions are considered.

You can always propose your own funded project, if you would like to contribute something of value that is not yet covered by an official bounty.