Map Knowledge Diffusion Across an Open-Source Project Ecosystem
Overview
What this challenge is about.
Build a contributor network from open-source data, trace a security fix across the graph, and identify key brokers. End with a verifiable certificate.
The scenario
The foundation is a non-profit steward in Brussels that hosts more than 400 community-run software projects and funds maintainers through annual community-investment grants, but it currently picks grant recipients from anecdote and mailing-list visibility rather than evidence of cross-project influence. The activity export in front of you covers sixty of its portfolio projects over a recent twenty-four-month period, with all real names, emails, and project identities replaced by stable pseudonymous codes.
The Brief
What you'll do, and what you'll demonstrate.
Determine, from network evidence, which ten contributors most drive the cross-project spread of engineering knowledge and security fixes, and whether funding them is defensible to a grants committee.
Earning criteria — what you'll demonstrate
- Construct a temporal bipartite contributor-project network from an event log and project it to a collaboration network
- Distinguish and correctly compute hub (eigenvector) versus broker (betweenness) roles over rolling time windows
- Detect and interpret community structure in an evolving collaboration network
- Trace a real diffusion event through network evidence rather than narrative assumption
- Translate quantitative network findings into a funding recommendation that withstands committee scrutiny
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Network Science
Apply network science to solve real industry problems and demonstrate production-level capability.
- Temporal Networks
Apply temporal networks to solve real industry problems and demonstrate production-level capability.
- Graph Analysis
Apply graph analysis to solve real industry problems and demonstrate production-level capability.
- Computational Social Science
Apply computational social science to solve real industry problems and demonstrate production-level capability.
- Data Analysis
Analyze real datasets, build models, and communicate findings that drive decisions.
- Centrality Analysis
Apply centrality analysis to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Computational Social Science Engineer
This challenge mirrors real work modeling human collaboration at scale: turning raw event logs into temporal networks, measuring influence, and defending findings to non-technical stakeholders who act on them.
This challenge sharpens
- temporal-networks
- computational-social-science
- network-science
Data Analyst (Network & Graph)
You practice the analyst's full loop — cleaning an export, computing graph metrics, validating stability, and writing a recommendation memo — which is exactly how graph-focused analysts support funding and prioritization decisions.
This challenge sharpens
- graph-analysis
- data-analysis
- centrality-analysis
Open-Source Program Strategy Analyst
Foundations and corporate OSS offices increasingly fund maintainers by evidence of influence; this challenge builds the network-science fluency needed to inform grant and investment decisions across a project ecosystem.
This challenge sharpens
- network-science
- centrality-analysis
- data-analysis