Author name disambiguation for collaboration network analysis and visualization
Bibliographic record
Abstract
Abstract In this paper we outline a heuristic algorithm for disambiguating author names of publications via deterministic clustering based on well‐defined similarity measures between publications in which their names appear as authors. The algorithm is designed to be used in the construction of a collaboration network, i.e., a graph of author nodes and co‐author links. In this context, the goal is to produce a co‐authorship graph with network characteristics that are close to those of the “true” collaboration network, so that meaningful network metrics can be determined. The algorithm we present here is fairly easily comprehended as it does not depend on any sophisticated AI techniques. This is important in the context of policy studies, in which we successfully applied it, as it enables policy makers to judge the soundness of the methodology with considerable confidence. It is also quite fast, making it possible to run large‐scale analyses (here, in the order of a hundred thousand publications and in the order of a million names to be disambiguated) on a moderately sized desktop computer within a few days. The algorithm is, finally, open to improvement via extensions that take into account additional kinds of fields in bibliographic records of publications to provide evidence that two occurrences of similar names belong to the same individual.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".