Map of GitHub

Original Article ↗ Hacker News Discussion ↗

Overall Reception

  • Many commenters find the visualization “phenomenal,” “artful,” and surprisingly usable and fast, even on mobile.
  • The playful country names (e.g., “Lispaña,” “Sussex,” “Homelabia,” “Quitlessia,” “The GitHub Archipelago”) are widely enjoyed and become a running joke.
  • Some treat it as a game: trying to locate specific projects without search or “sailing” from one project to another via paths.

Data Source, Similarity, and Layout

  • Repos are positioned based on overlapping stargazers. Dots are close if they share many stargazers.
  • Edges between repos are derived from a similarity metric, primarily Jaccard similarity over star sets, with a threshold to decide which edges exist (exact threshold not specified).
  • Lines only appear when zoomed into a region.
  • Popular “celebrity” projects tend to cluster together due to generic popularity rather than semantic similarity; commenters note this as a known limitation.
  • Suggestions include using TF–IDF over the user–star matrix to downweight “overstarring” users, or code embeddings, though resource costs are questioned.
  • The author experimented with multiple similarity metrics and chose Jaccard subjectively as “best” for this use.
  • Clustering uses community-detection–style algorithms (Louvain/Leiden plus custom methods). Hierarchical clustering ideas (e.g., HDBSCAN) ran into memory issues at this scale.

Interpretation Quirks and Ecosystem Insights

  • Several projects appear in “unexpected” lands (e.g., Linux near frontend/awesome lists, HTMX in Djangonia, Django in Pythonia, MicroPython/CircuitPython placement, Magisk forks in different regions).
  • Explanations offered:
    • Users star surrounding ecosystem projects more than core ones (e.g., Linux kernel, Django).
    • “Aspirational” star patterns (e.g., people star Julia alongside Python ML/AI projects without fully moving ecosystems).
    • Overlap of interest communities (e.g., crypto with AI).
  • Some observe smaller-than-expected regions for Rust, Node, or Azure, and very large ones for JavaScript, YAML/DevOps, Python/AI, Vim/Emacs.
  • One hypothesis: ecosystems with lower friction to publishing packages (e.g., JavaScript) yield larger “islands.”
  • PHP’s prominent “kingdom” is noted as evidence it remains widely used and actively developed.

Critiques of the Map Metaphor and Stars

  • Some question the country/map metaphor and fuzzy region names; they propose hierarchical cluster diagrams with clearer labels.
  • Others appreciate that it is “just a view, not a thesis,” and like the personality over stricter analytical clarity.
  • Skeptics note that stars can be noisy or gamed (bots, vanity projects), so importance and quality are not faithfully represented.