Mentored Projects#

This page maintains a list of mentored project ideas that contributors can work on if they are interested in contributing to the NetworkX project. Feel free to suggest any other idea if you are interested on the NetworkX GitHub discussions page

These ideas can be used as projects for Google Summer of Code, Outreachy, NumFOCUS Small Development Grants and university course/project credits (if your university allows contribution to open source for credit).

Pedagogical Interactive Notebooks for Algorithms Implemented in NetworkX#

  • Abstract: NetworkX has a wide variety of algorithms implemented. Even though the algorithms are well documented, explanations of the ideas behind the algorithms are often missing and we would like to collect these, write Jupyter notebooks to elucidate these ideas and explore the algorithms experimentally, and publish the notebooks at networkx/notebooks. The goal is to gives readers a deeper outlook behind standard network science and graph theory algorithms and encourage them to delve further into the topic.

  • Recommended Skills: Python, Jupyter notebooks, graph algorithms.

  • Expected Outcome: A collection of Interactive Jupyter notebooks which explain and explore network algorithms to readers and users of NetworkX. For example, see this notebook on Geometric Generator Models

  • Complexity: Depending on the algorithms you are interested to work on.

  • Interested Mentors: @MridulS, @rossbar

  • Expected time commitment: This project can be either a medium project (~175 hours) or a large project (~350 hours). The contributor is expected to contribute 2-3 pedagogical interactive notebooks for the medium duration project and 4-5 notebooks for the long duration project.

Visualization API with Matplotlib#

  • Abstract: NetworkX has some basic drawing tools that use Matplotlib to render the images. The API hasn’t changed while Matplotlib has changed. Also we have added or are trying to add new features especially with regard to plotting edges. We’d like someone to read a lot about what we offer and also what Matplotlib offers, and come up with a nice way for users to draw graphs flexibly and yet with good defaults. There is little chance just a broad topic could be completed in one summer, but a roadmap and substantial headway on that road is possible.

  • Recommended Skills: Python, matplotlib experience.

  • Expected Outcome: A roadmap for a refined API for the matplotlib tools within NetworkX as well as code in the form of PR(s) which implement (part of) that API with tests.

  • Interested Mentors: @dschult, @rossbar

  • Expected time commitment: This project will be a full time 10 week project (~350 hrs).

Incorporate a Python library for ISMAGs isomorphism calculations#

  • Abstract: A team from Sandia Labs has converted the original java implementation of the ISMAGS isomorphism routines to Python. They have invited us to incorporate that code into NetworkX if we are interested. We’d like someone to learn the ISMAGS code we currently provide, and the code from this new library and figure out what the best combination is to include in NetworkX moving forward. That could be two separate subpackages of tools, or more likely a combination of the two sets of code, or a third incantation that combines good features from each.

  • Recommended Skills: Python, graph algorithms.

  • Expected Outcome: A plan for how to best incorporate ISMAGS into NetworkX along with code to do that incorporation.

  • Interested Mentors: @dschult, @rossbar

  • Expected time commitment: This project will be a full time 10 week project (~350 hrs).

Centrality Atlas#

  • Abstract: The goal of this project would be to produce a comprehensive review of network centrality measures. Centrality is a central concept in network science and has many applications across domains. NetworkX provides many functions for measuring various types of network centrality. The individual centrality functions are typically well-described by their docstrings (though there’s always room for improvement!); however, there currently is no big-picture overview of centrality. Furthermore, many of the centrality measures are closely related, but there is no documentation that describes these relationships.

  • Recommended Skills: Python, literature review, technical writing

  • Expected Outcome: An executable document that provides an overview and applications of network centrality measures. Potential outputs include (but are not limited to): an article for nx-guides (see above) and/or an example gallery for centrality measures.

  • Interested Mentors: @dschult, @rossbar

  • Expected time commitment: Variable, though a high-quality review article would be expected to take several months of dedicated research (~350 hours).

Completed Projects#