{"id":"https://openalex.org/W2903425083","doi":"https://doi.org/10.1109/icpr.2018.8545389","title":"Common Random Subgraph Modeling Using Multiple Instance Learning","display_name":"Common Random Subgraph Modeling Using Multiple Instance Learning","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2903425083","doi":"https://doi.org/10.1109/icpr.2018.8545389","mag":"2903425083"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545389","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545389","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"article","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/A5100621905","display_name":"Tao Xu","orcid":"https://orcid.org/0000-0001-7268-6054"},"institutions":[{"id":"https://openalex.org/I79817857","display_name":"University of Guelph","ror":"https://ror.org/01r7awg59","country_code":"CA","type":"education","lineage":["https://openalex.org/I79817857"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Tao Xu","raw_affiliation_strings":["School of Computer, Science University of Guelph"],"affiliations":[{"raw_affiliation_string":"School of Computer, Science University of Guelph","institution_ids":["https://openalex.org/I79817857"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102819493","display_name":"David Chiu","orcid":"https://orcid.org/0000-0002-1210-8268"},"institutions":[{"id":"https://openalex.org/I79817857","display_name":"University of Guelph","ror":"https://ror.org/01r7awg59","country_code":"CA","type":"education","lineage":["https://openalex.org/I79817857"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"David Chiu","raw_affiliation_strings":["School of Computer, Science University of Guelph"],"affiliations":[{"raw_affiliation_string":"School of Computer, Science University of Guelph","institution_ids":["https://openalex.org/I79817857"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044883854","display_name":"Iker Gondra","orcid":"https://orcid.org/0000-0002-5366-4826"},"institutions":[{"id":"https://openalex.org/I197191942","display_name":"St. Francis Xavier University","ror":"https://ror.org/01wcaxs37","country_code":"CA","type":"education","lineage":["https://openalex.org/I197191942"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Iker Gondra","raw_affiliation_strings":["Mathematics, St. Francis Xavier University"],"affiliations":[{"raw_affiliation_string":"Mathematics, St. Francis Xavier University","institution_ids":["https://openalex.org/I197191942"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100621905"],"corresponding_institution_ids":["https://openalex.org/I79817857"],"apc_list":null,"apc_paid":null,"fwci":0.1045,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.47375534,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"1205","last_page":"1210"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9973999857902527,"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/T11106","display_name":"Data Management and Algorithms","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.6444845199584961},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5772335529327393},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5252154469490051},{"id":"https://openalex.org/keywords/random-graph","display_name":"Random graph","score":0.515219509601593},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.49791741371154785},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4460570812225342},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44167301058769226},{"id":"https://openalex.org/keywords/induced-subgraph-isomorphism-problem","display_name":"Induced subgraph isomorphism problem","score":0.4249001443386078},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4098319411277771},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.40136560797691345},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37566709518432617},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.0892440676689148}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6444845199584961},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5772335529327393},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5252154469490051},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.515219509601593},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.49791741371154785},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4460570812225342},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44167301058769226},{"id":"https://openalex.org/C191241153","wikidata":"https://www.wikidata.org/wiki/Q6027240","display_name":"Induced subgraph isomorphism problem","level":5,"score":0.4249001443386078},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4098319411277771},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40136560797691345},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37566709518432617},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.0892440676689148},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545389","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545389","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1492230849","https://openalex.org/W1728282434","https://openalex.org/W1971105294","https://openalex.org/W1996551363","https://openalex.org/W1997569534","https://openalex.org/W2010792435","https://openalex.org/W2081605842","https://openalex.org/W2154318594","https://openalex.org/W6629460376","https://openalex.org/W6683033130"],"related_works":["https://openalex.org/W231720905","https://openalex.org/W1482551403","https://openalex.org/W2532922352","https://openalex.org/W2128390795","https://openalex.org/W2915540008","https://openalex.org/W2604893261","https://openalex.org/W3002471523","https://openalex.org/W3108150460","https://openalex.org/W3102805201","https://openalex.org/W2793671626"],"abstract_inverted_index":{"In":[0],"balancing":[1],"information":[2,40],"that":[3,44,81,128],"is":[4,45,68,112],"typical":[5],"of":[6,26,41,47,65,70,92,141],"a":[7,17,48,133,142],"class":[8,49],"and":[9,38,67,135],"discriminatory":[10],"between":[11,85,101,155],"classes,":[12],"we":[13],"aim":[14],"at":[15,87,106],"synthesizing":[16],"common":[18,31,46,137],"random":[19,32,138],"subgraph":[20,33,139],"(CRSG)":[21],"model":[22,34,140],"from":[23,119],"an":[24,58],"ensemble":[25],"attributed":[27],"graph":[28,73,102,122],"data.":[29],"The":[30,75,110,124],"incorporates":[35],"both":[36],"structural":[37,99,116],"probabilistic":[39],"the":[42,51,83,107,120,129],"data":[43,84],"in":[50,61,147,153],"ensemble,":[52],"while":[53],"multiple":[54,78],"instance":[55,79],"learning":[56,80],"provides":[57],"effective":[59,146],"process":[60],"handling":[62],"large":[63],"number":[64],"samples":[66],"tolerant":[69],"substantial":[71],"irrelevant":[72],"elements.":[74],"proposed":[76],"two-level":[77],"compares":[82],"graphs":[86],"one":[88],"level":[89],"(as":[90,104],"bags":[91],"instances),":[93],"but":[94,144],"also":[95,145],"takes":[96],"into":[97],"account":[98],"relationships":[100],"elements":[103],"instances)":[105],"other":[108],"level.":[109],"method":[111,130],"evaluated":[113],"using":[114],"benchmarked":[115],"datasets":[117],"taken":[118],"IAM":[121],"repository.":[123],"experimental":[125],"results":[126],"show":[127],"can":[131],"generate":[132],"meaningful":[134],"informative":[136],"class,":[143],"applying":[148],"it":[149],"to":[150],"classification":[151],"tasks":[152],"discriminating":[154],"classes.":[156]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}