Scaling Author Identity Disambiguation to the World of Code: A Methodology

• URL: http://arxiv.org/abs/2607.06920v1
• Date: Wed, 08 Jul 2026 02:30:26 GMT
• Title: Scaling Author Identity Disambiguation to the World of Code: A Methodology
• Abstract summary: We describe the methodology used to alias the World of Code (WoC) collection (version V2604, 107M distinct author strings over 6B commits) into canonical persons.<n>At this scale the central problem is over-merge, not missed merges.<n>We report the full experimental record (more than twenty experiments, including unsuccessful ones) behind the deployed design.
• Score: 2.788646205877688
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We describe the methodology used to alias the free-text author/committer identities of the entire World of Code (WoC) collection (version V2604, ~107M distinct author strings over ~6B commits) into canonical persons, extending the fingerprint-based anti-aliasing of ALFAA and the 38M-identity resolution of Fry et al. by an order of magnitude. At this scale the central problem is over-merge, not missed merges: a few bridge identities (bots, role accounts, placeholder emails, multi-author commit fields) transitively weld unrelated clusters through the global union step into million-member "mega-clusters." We report the full experimental record (more than twenty experiments, including unsuccessful ones) behind the deployed design. Node-level gates (information score, project spread, degree) preserve recall but cannot dissolve the mega-cluster; per-value blocklists of high-quality-but-shared attributes are recall-safe but cannot break a redundant mesh; the working composition is a betweenness cut over the exact union graph plus a per-edge classifier trained on 2.6M labels mined from GitHub no-reply identifiers. That classifier, filtering dormant cross-project shingle groups and joined by GitHub's own account assertions, then recovers the recall the precision work had foregone. Against human-adjudicated pairs the per-edge model transfers at AUC 0.99; end to end the largest cluster falls from 170,431 (and a predecessor's 3.0M) to under 7,000 w hile gold recall rises from 0.44 to 0.70 at increasing precision, and on an independent 21M-alias GitHub ground truth the final map outscores its predecessors and the published state of the art among global, privacy-preserving resolvers. The record doubles as a catalog of scale lessons: structural cuts do not transfer to edge sets they never saw, and recall-only and precision-only benchmarks invert verdicts unless read together.

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