Node similarity in networked information spaces
Bibliographic record
Abstract
Netvorked information spaces contain information entities, corresponding to nodes, vhich are (:orlrle(:l.ed [)y associm.i(ms, (:or'r'esporldirlg 1.o links irl (.he nel.wor'k. Exarrq)les of nel.wor'ked information spaces are: the World Wide Web, vhere information entities are veb pages, and associations are hyperlinks; the scientific literature, vhere information entities are articles and associations are references to other articles. SimilariW betveen information entities in a net- vorked information space can be defined not only based on the content of the information entities, but also based on the connectivity established by the associations present. This paper explores the definition of similariW based on connectivity only, and proposes several algorithms [)r' I, his [mr'pose. Our' rrlei, r'i(:s I,ake mJvard,age o[' I, he local rleigh[)or'hoo(ts o[' I, he rmcJes irl I, he rlel,- is no required, as long as a query engine is available for fo]]oving ]inks and extracting he necessary local neighbourhoods for similarity estimation. Tvo variations of similarity estimation beveen vo nodes are described, one based on he separate local neighbourhoods of he nodes, and another based on he join local neighbourhood expanded from boh nodes a he same ime. The algorithms are imp]emened and evaluated on he citation graph of computer science. The immediate application of his vork is in finding papers similar o a given paper [he Web.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".