Map Knowledge Diffusion in a Global Open-Source Ecosystem
Overview
What this challenge is about.
Pull (or use a provided anonymized export of) the last 24 months of commit + PR + issue data for 60 projects in the foundation's portfolio. Build a temporal contributor-project bipartite graph and project it to a contributor-contributor co-authorship graph. Compute: betweenness centrality (brokers), eigenvector centrality (hubs), and community structure (Leiden) over rolling 6-month windows. Map a known diffusion event (e.g., adoption of a specific security fix across 12 projects) as a case study. Deliver: 14-page study, a small Plotly Dash dashboard, and a 6-page grants-recommendation memo identifying 10 contributors whose work the foundation should fund.
The Brief
What you'll do, and what you'll demonstrate.
Use temporal-network analysis to identify hub and broker contributors driving cross-project knowledge diffusion, and recommend 10 grant recipients.
Earning criteria — what you'll demonstrate
- Build temporal bipartite networks from version-control data
- Apply centrality measures on rolling windows, not single snapshots
- Identify broker contributors using betweenness over time
- Translate network insights into funding allocation
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Product Manager
Product managers who can read a network analysis and translate it into community-investment decisions become the trusted PM for developer-relations teams.
This challenge sharpens
- computational-social-science
- centrality-analysis
- data-analysis