Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicComplex Network Analysis Techniques
Canadian institutionsnot available
FundersOtto von Guericke University MagdeburgInstituto Superior TécnicoInstitute for Infocomm ResearchUniversity of Massachusetts AmherstUniversity of California, Los AngelesTechnische Universität ClausthalSingapore Management UniversityNational Laboratory of Pattern RecognitionHaute école Spécialisée de Suisse OccidentaleGuangxi Normal UniversityKU LeuvenUniversity of Massachusetts BostonPeking UniversityUniversidad de CórdobaUniversidade Federal de Minas GeraisTampereen YliopistoUniversidad de Castilla-La ManchaUniversity of AlbertaKorea Advanced Institute of Science and TechnologyYonsei UniversityDalian University of TechnologyUniversität KonstanzKangwon National UniversityTrakya ÜniversitesiHebrew University of JerusalemBauhaus-Universität WeimarUniversity of TsukubaUniversity of Central FloridaUniversidad de GranadaUniversidade de LisboaTechnische Universität DarmstadtUniversiteit AntwerpenUniversidad Autónoma de MadridUniversiteit GentSapienza Università di RomaUniversità degli Studi di PadovaUniversiteit van AmsterdamUniversité de FribourgFudan UniversityNational Central UniversityEwha Womans UniversityKyung Hee UniversityHanyang UniversityUniversität HeidelbergTechnische Universität BerlinBeihang UniversityUniversity of TwenteNational Cheng Kung UniversityMicrosoft Research AsiaCity University of Hong KongUniversity of WaterlooRadboud UniversiteitCurtin University of TechnologyAccentureQueensland University of TechnologyYork UniversityUniversity of GlasgowUniversity of LouisvilleUniversità della CalabriaUniversity of CreteArizona State UniversityUniversitat Pompeu FabraNational Chengchi UniversityFlorida Institute of TechnologyIndian Institute of Technology MadrasUniversity of WarwickRMIT UniversityUniversità degli Studi di SienaUniversità degli Studi di MilanoDublin City UniversityJohns Hopkins UniversityHarbin Institute of TechnologyUniversity of Illinois at Urbana-ChampaignMicrosoftKent State UniversityShandong UniversityHong Kong Baptist UniversityMissouri University of Science and TechnologyUniversity of BedfordshireIndian Institute of Technology BombayUniversidad de ValladolidTeesside UniversityRobert Gordon UniversityUniversity of WolverhamptonDartmouth CollegeNational ICT AustraliaWashington State UniversityDrexel UniversityUniversità degli Studi di VeronaCarnegie Mellon UniversityQueen Mary University of LondonUniversity of OttawaUniversity of Southern CaliforniaUniversity of OklahomaGeorgia Institute of TechnologyUniversidade da CoruñaDalhousie UniversityDePaul UniversityUniversity of Texas at ArlingtonUniversity of OregonUniversity of Technology SydneyTechnische Universiteit DelftMicrosoft ResearchUniversité de ToulouseUniversity of OxfordFlorida International UniversityUniversity of PittsburghJulius-Maximilians-Universität Würzburg
KeywordsComputer scienceNavigabilityInformation retrievalHierarchyInterface (matter)Tag systemVisibilityFolksonomySocial network (sociolinguistics)World Wide WebSocial mediaAlgorithm
Abstract
fetched live from OpenAlexToday, a number of algorithms exist for constructing tag hierarchies from social tagging data. While these algorithms were designed with ontological goals in mind, we know very little about their properties from an information retrieval perspective, such as whether these tag hierarchies support efficient navigation in social tagging systems. The aim of this paper is to investigate the usefulness of such tag hierarchies (sometimes also called folksonomies - from folk-generated taxonomy) as directories that aid navigation in social tagging systems. To this end, we simulate navigation of directories as decentralized search on a network of tags using Kleinberg's model. In this model, a tag hierarchy can be applied as background knowledge for decentralized search. By constraining the visibility of nodes in the directories we aim to mimic typical constraints imposed by a practical user interface (UI), such as limiting the number of displayed subcategories or related categories. Our experiments on five different social tagging datasets show that existing tag hierarchy algorithms can support navigation in theory, but our results also demonstrate that they face tremendous challenges when user interface (UI) restrictions are taken into account. Based on this observation, we introduce a new algorithm that constructs efficiently navigable directories on our datasets. The results are relevant for engineers and scientists aiming to improve navigability of social tagging systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.285
Codex and Gemma teacher scores by category
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.
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
