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). Mentee/contributors participating under NetworkX organisation would be expected to share their weekly work updates and get feedback in a 1-hr weekly meetings. If this isn’t feasible for the contributor we can discuss further to figure something else out.

If you are a professor interested in having your class develop tools and contribute the results to NetworkX, we welcome your submissions! We encourage you to consider rewriting/improving existing functions as a potential source of projects. Working off of and improving existing tools involves reading, evaluating and writing code, rather than just writing new code. We also encourage them to review each other’s PRs. You can have students submit their PRs to your personal fork, discuss, review, etc in an environment conducive to mentoring and learning. Once their branch is ready to merge, it can be submitted to the main NetworkX repository. This will help keep in-class discussions separate from the broader NetworkX review process, making both more manageable and readable. Feel free to reach out to use as you plan these activities.

Adding embarrassingly parallel graph algorithms in nx-parallel#

  • Abstract: nx-parallel is a NetworkX backend that uses joblib for implementing parallel graph algorithms. Currently, only some of the NetworkX algorithms are implemented in nx-parallel. We expect the contributor to find embarrassingly parallel graph algorithms from the wide variety of graph algorithms implemented in NetworkX and then write their parallel implementations in nx-parallel. You can start by looking at:

    Find more details in Issue#82.

  • Recommended Skills: Python, willingness to roll up your sleeves and dig deep and understand nx-parallel’s infrastructure, and ability to take feedback and iterate on your work.

  • Expected Outcome: 3 parallel graph algorithms (~175 hours), or 7 (~350 hours), implemented in nx-parallel.

  • Complexity: Medium

  • Interested Mentors: @Schefflera-Arboricola, @dschult

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, @Schefflera-Arboricola

  • 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.

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, @Schefflera-Arboricola

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

Completed Projects#