{"id":"https://openalex.org/W3206436334","doi":"https://doi.org/10.1007/s10618-024-01065-4","title":"Random walks with variable restarts for negative-example-informed label propagation","display_name":"Random walks with variable restarts for negative-example-informed label propagation","publication_year":2024,"publication_date":"2024-08-13","ids":{"openalex":"https://openalex.org/W3206436334","doi":"https://doi.org/10.1007/s10618-024-01065-4","mag":"3206436334"},"language":"en","primary_location":{"id":"doi:10.1007/s10618-024-01065-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-024-01065-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-024-01065-4.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10618-024-01065-4.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055430350","display_name":"Sean Maxwell","orcid":"https://orcid.org/0000-0002-8924-8111"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sean Maxwell","raw_affiliation_strings":["Department of Computer Science and Data Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA"],"raw_orcid":"https://orcid.org/0000-0002-8924-8111","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Data Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035558591","display_name":"Mehmet Koyut\u00fcrk","orcid":"https://orcid.org/0000-0002-3434-5512"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mehmet Koyut\u00fcrk","raw_affiliation_strings":["Department of Computer Science and Data Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Data Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA","institution_ids":["https://openalex.org/I58956616"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5055430350"],"corresponding_institution_ids":["https://openalex.org/I58956616"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00129171,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"38","issue":"6","first_page":"4024","last_page":"4039"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9904999732971191,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.7338348031044006},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.7326799631118774},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6430292129516602},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6289831399917603},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6106935739517212},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.5755994319915771},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.528387725353241},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5149946808815002},{"id":"https://openalex.org/keywords/belief-propagation","display_name":"Belief propagation","score":0.4201357960700989},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4114611744880676},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40441855788230896},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.35147953033447266},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2844929099082947},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16012361645698547}],"concepts":[{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.7338348031044006},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.7326799631118774},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6430292129516602},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6289831399917603},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6106935739517212},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.5755994319915771},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.528387725353241},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5149946808815002},{"id":"https://openalex.org/C152948882","wikidata":"https://www.wikidata.org/wiki/Q4060686","display_name":"Belief propagation","level":3,"score":0.4201357960700989},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4114611744880676},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40441855788230896},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35147953033447266},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2844929099082947},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16012361645698547},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10618-024-01065-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-024-01065-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-024-01065-4.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},{"id":"pmh:oai:commons.case.edu:staffworks-1038","is_oa":true,"landing_page_url":"https://commons.case.edu/staffworks/39","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Researchers, Instructors, & Staff Scholarship","raw_type":"text"}],"best_oa_location":{"id":"doi:10.1007/s10618-024-01065-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-024-01065-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-024-01065-4.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3206436334.pdf"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W61101988","https://openalex.org/W1907790026","https://openalex.org/W1908728294","https://openalex.org/W2006004162","https://openalex.org/W2063049279","https://openalex.org/W2107569009","https://openalex.org/W2132202037","https://openalex.org/W2152284345","https://openalex.org/W2153959628","https://openalex.org/W2154455818","https://openalex.org/W2360309522","https://openalex.org/W2412975798","https://openalex.org/W2514846838","https://openalex.org/W2624021832","https://openalex.org/W2783405727","https://openalex.org/W2803971899","https://openalex.org/W2900763475","https://openalex.org/W2961187130","https://openalex.org/W2963168676","https://openalex.org/W2964051675","https://openalex.org/W2975966238","https://openalex.org/W3002924435","https://openalex.org/W3039500550","https://openalex.org/W3046714933","https://openalex.org/W3101543043","https://openalex.org/W3120587221","https://openalex.org/W3133130163","https://openalex.org/W3175539961","https://openalex.org/W4319653823"],"related_works":["https://openalex.org/W4238897586","https://openalex.org/W435179959","https://openalex.org/W2619091065","https://openalex.org/W2059640416","https://openalex.org/W1490753184","https://openalex.org/W2284465472","https://openalex.org/W2291782699","https://openalex.org/W1993948687","https://openalex.org/W2000169967","https://openalex.org/W2112883198"],"abstract_inverted_index":{"Abstract":[0],"Label":[1],"propagation":[2,28,128,192],"is":[3,134,148],"frequently":[4],"encountered":[5],"in":[6,117,189],"machine":[7],"learning":[8,69],"and":[9,82,101,126,166,193,213],"data":[10],"mining":[11],"applications":[12],"on":[13,68,151,184],"graphs,":[14],"either":[15],"as":[16,21,91],"a":[17,141,145],"standalone":[18],"problem":[19],"or":[20,70,210],"part":[22],"of":[23,58,96,111,138,177],"node":[24,143,147,194],"classification.":[25],"Many":[26],"label":[27,57,191],"algorithms":[29,61,205],"utilize":[30],"random":[31,122],"walks":[32,77],"(or":[33],"network":[34,113,127,185],"propagation),":[35],"which":[36],"provide":[37],"limited":[38],"ability":[39],"to":[40,51,62,75,98,129,169,217],"take":[41],"into":[42],"account":[43],"negatively-labeled":[44,64,80,118,146,171],"nodes":[45,47,65,81],"(i.e.,":[46],"that":[48,114,133,160,200,206,214],"are":[49,115,220],"known":[50],"be":[52,99],"not":[53,103],"associated":[54],"with":[55,124],"the":[56,72,94,106,112,135,175],"interest).":[59],"Specialized":[60],"incorporate":[63],"generally":[66,221],"focus":[67],"readjusting":[71],"edge":[73,167,208],"weights":[74,209],"drive":[76,105],"away":[78,108],"from":[79,109],"toward":[83],"positively-labeled":[84,142],"nodes.":[85,119,172],"This":[86],"approach":[87],"has":[88],"several":[89],"disadvantages,":[90],"it":[92],"increases":[93],"number":[95],"parameters":[97],"learned,":[100],"does":[102],"necessarily":[104],"walk":[107,123],"regions":[110],"rich":[116],"We":[120],"reformulate":[121],"restarts":[125],"enable":[130],"\u201cvariable":[131],"restarts\",":[132],"increased":[136],"likelihood":[137],"restarting":[139],"at":[140],"when":[144],"encountered.":[149],"Based":[150],"this":[152],"reformulation,":[153],"we":[154,180],"develop":[155],"CusTaRd":[156,178,201],",":[157,179],"an":[158],"algorithm":[159],"effectively":[161],"combines":[162],"variable":[163],"restart":[164,211],"probabilities":[165],"re-weighting":[168],"avoid":[170],"To":[173],"assess":[174],"performance":[176],"perform":[181],"comprehensive":[182],"experiments":[183],"datasets":[186],"commonly":[187],"used":[188],"benchmarking":[190],"classification":[195],"algorithms.":[196],"Our":[197],"results":[198],"show":[199],"consistently":[202],"outperforms":[203],"competing":[204],"learn":[207],"profiles,":[212],"negatives":[215],"close":[216],"positive":[218],"examples":[219],"more":[222,225],"informative":[223],"than":[224],"distant":[226],"negatives.":[227]},"counts_by_year":[],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}