Discovering Emerging Open Source Communities Through...
summary
Project data comes from GitHub via Augur and feeds an Aspen dashboard; repositories and contributors become nodes and edges where shared activities (PRs issues commits reviews) set edge weights and planned weighting distinguishes maintainers from casual contributors. Implemented Python notebooks apply PageRank, betweenness centrality and closeness to ~75 CNCF repos (2020–2023), scoring graduated projects higher and sandbox lower, flagging OpenShift as tightly linked to Kubernetes, and yielding early signals of sustainability to feed future ML trajectory models.
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chapters (19)
- 0:00Open source discovery intro
- 1:07Project goals survey
- 2:58Network analysis for ecosystems
- 6:54Data sources connections
- 7:16Graph fundamentals
- 9:06Visualizing repo links
- 10:07Shared activity in networks
- 11:10Finding emerging projects
- 11:56Audience insights on emergence
- 13:42Key insights to track
- 15:28Graph centrality algorithms
- 16:31PageRank for ranking
- 18:40Betweenness centrality
- 20:25Closeness centrality
- 22:11Case study OpenShift
- 25:21CNCF projects by maturity
- 28:37Algorithm scores filtering
- 30:43Future predictions efforts
- 32:34Q&A
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