{"id":"https://openalex.org/W2046150178","doi":"https://doi.org/10.1145/2480362.2480387","title":"Out-of-bag discriminative graph mining","display_name":"Out-of-bag discriminative graph mining","publication_year":2013,"publication_date":"2013-03-18","ids":{"openalex":"https://openalex.org/W2046150178","doi":"https://doi.org/10.1145/2480362.2480387","mag":"2046150178"},"language":"en","primary_location":{"id":"doi:10.1145/2480362.2480387","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2480362.2480387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076237050","display_name":"Andreas Maunz","orcid":"https://orcid.org/0000-0002-2784-9456"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andreas Maunz","raw_affiliation_strings":["Institute for Physics, Freiburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Physics, Freiburg, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020178686","display_name":"David Vorgrimmler","orcid":null},"institutions":[{"id":"https://openalex.org/I4210108360","display_name":"In Silico Toxicology (Switzerland)","ror":"https://ror.org/01ssr1j70","country_code":"CH","type":"company","lineage":["https://openalex.org/I4210108360"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"David Vorgrimmler","raw_affiliation_strings":["In-silico Toxicology, Basel, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"In-silico Toxicology, Basel, Switzerland","institution_ids":["https://openalex.org/I4210108360"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066862195","display_name":"Christoph Helma","orcid":"https://orcid.org/0000-0002-2640-798X"},"institutions":[{"id":"https://openalex.org/I4210108360","display_name":"In Silico Toxicology (Switzerland)","ror":"https://ror.org/01ssr1j70","country_code":"CH","type":"company","lineage":["https://openalex.org/I4210108360"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Christoph Helma","raw_affiliation_strings":["In-silico Toxicology, Basel, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"In-silico Toxicology, Basel, Switzerland","institution_ids":["https://openalex.org/I4210108360"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"109","last_page":"114"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11719","display_name":"Data Quality and Management","score":0.9789000153541565,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9677000045776367,"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/discriminative-model","display_name":"Discriminative model","score":0.8934229612350464},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6976265907287598},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5487830638885498},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5365912914276123},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.391539990901947},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36767905950546265},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.3250625729560852},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.315277099609375},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.280287504196167}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8934229612350464},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6976265907287598},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5487830638885498},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5365912914276123},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.391539990901947},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36767905950546265},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.3250625729560852},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.315277099609375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.280287504196167},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2480362.2480387","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2480362.2480387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W98862427","https://openalex.org/W1618035246","https://openalex.org/W1977340881","https://openalex.org/W1985104873","https://openalex.org/W2019139558","https://openalex.org/W2099962227","https://openalex.org/W2148611932","https://openalex.org/W2149048950","https://openalex.org/W2162481448","https://openalex.org/W2328870692"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W1989889224","https://openalex.org/W4382618745","https://openalex.org/W1973775000","https://openalex.org/W2748922771","https://openalex.org/W1987128138","https://openalex.org/W2743976221"],"abstract_inverted_index":{"In":[0,38,144],"class-labeled":[1],"graph":[2,5,51,71,124,185],"databases,":[3,134,200],"each":[4,74],"is":[6,78,109,157,188],"associated":[7,72],"with":[8,30,73],"one":[9],"from":[10,104,162],"a":[11,50,66,70,158,190],"finite":[12],"set":[13],"of":[14,49,59,64,84,99,122,148,151,171,195,204],"classes,":[15],"which":[16,187],"induces":[17],"associations":[18,33,165],"between":[19],"classes":[20],"and":[21,92,135,141,201],"subgraphs":[22,29,42,149,172],"occurring":[23],"in":[24,53,69,192],"the":[25,81,85,97,100,116,128,146,163,169,175,193,205],"database":[26,52],"graphs.":[27],"The":[28,62],"strong":[31],"class":[32,75,139,142],"are":[34,43,178],"called":[35],"discriminative":[36,41,123,184],"subgraphs.":[37],"this":[39],"work,":[40],"repeatedly":[44],"mined":[45],"on":[46,57,115],"bootstrap":[47,86],"samples":[48],"order":[54],"to":[55,119,130,198],"improve":[56,114],"estimation":[58,95],"subgraph":[60,67,164],"associations.":[61],"number":[63,170],"times":[65],"occurs":[68],"(support":[76],"values)":[77],"recorded":[79],"over":[80],"out-of-bag":[82],"instances":[83],"process.":[87],"We":[88],"investigate":[89],"sample":[90],"mean":[91],"maximum":[93],"likelihood":[94],"for":[96,182],"approximation":[98],"true":[101],"underlying":[102],"support":[103,137],"these":[105],"empirical":[106],"values.":[107],"It":[108],"shown":[110],"that":[111,154],"both":[112],"significantly":[113],"process,":[117],"compared":[118],"single":[120],"runs":[121],"mining,":[125,186],"by":[126,174],"applying":[127],"methods":[129,177],"publicly":[131],"available":[132],"toxicological":[133],"validating":[136],"values,":[138],"bias,":[140],"significance.":[143],"toxicology,":[145],"detection":[147],"(fragments":[150],"chemical":[152],"structure)":[153],"induce":[155],"toxicity":[156],"major":[159],"goal.":[160],"Apart":[161],"being":[166],"statistically":[167],"validated,":[168],"created":[173],"proposed":[176],"much":[179],"lower":[180],"than":[181],"ordinary":[183],"often":[189],"bottleneck":[191],"application":[194],"computational":[196],"models":[197],"such":[199],"hinders":[202],"interpretation":[203],"results.":[206]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
