{"id":"https://openalex.org/W2114062029","doi":"https://doi.org/10.1145/1807167.1807262","title":"GAIA","display_name":"GAIA","publication_year":2010,"publication_date":"2010-06-06","ids":{"openalex":"https://openalex.org/W2114062029","doi":"https://doi.org/10.1145/1807167.1807262","mag":"2114062029"},"language":"en","primary_location":{"id":"doi:10.1145/1807167.1807262","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1807167.1807262","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2010 ACM SIGMOD International Conference on Management of data","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/A5064463976","display_name":"Jin Ning","orcid":"https://orcid.org/0000-0002-8551-7038"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ning Jin","raw_affiliation_strings":["University of North Carolina at Chapel Hill, Chapel Hill, NC, USA","University of North Carolina at Chapel Hill , Chapel Hill, NC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA","institution_ids":["https://openalex.org/I114027177"]},{"raw_affiliation_string":"University of North Carolina at Chapel Hill , Chapel Hill, NC, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059912644","display_name":"C. A. Young","orcid":"https://orcid.org/0000-0001-5966-5697"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Calvin Young","raw_affiliation_strings":["University of North Carolina at Chapel Hill, Chapel Hill, NC, USA","University of North Carolina at Chapel Hill , Chapel Hill, NC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA","institution_ids":["https://openalex.org/I114027177"]},{"raw_affiliation_string":"University of North Carolina at Chapel Hill , Chapel Hill, NC, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100392089","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-8180-2886"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["University of North Carolina at Chapel Hill, Chapel Hill, NC, USA","University of North Carolina at Chapel Hill , Chapel Hill, NC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA","institution_ids":["https://openalex.org/I114027177"]},{"raw_affiliation_string":"University of North Carolina at Chapel Hill , Chapel Hill, NC, USA","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.7811,"has_fulltext":false,"cited_by_count":78,"citation_normalized_percentile":{"value":0.97296414,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"879","last_page":"890"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.9997000098228455,"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":0.9997000098228455,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9922000169754028,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9842000007629395,"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":0.9196592569351196},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7446929812431335},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6943866610527039},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5980602502822876},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5965699553489685},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4331423044204712},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4301833510398865},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38982754945755005},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2772875726222992},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.19169756770133972},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.12483179569244385}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9196592569351196},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7446929812431335},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6943866610527039},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5980602502822876},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5965699553489685},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4331423044204712},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4301833510398865},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38982754945755005},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2772875726222992},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.19169756770133972},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.12483179569244385}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1807167.1807262","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1807167.1807262","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2010 ACM SIGMOD International Conference on Management of data","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.75}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1524882660","https://openalex.org/W1535228720","https://openalex.org/W1641749581","https://openalex.org/W1957053378","https://openalex.org/W2102039273","https://openalex.org/W2110920461","https://openalex.org/W2118349699","https://openalex.org/W2119396705","https://openalex.org/W2125571188","https://openalex.org/W2126359798","https://openalex.org/W2136593687","https://openalex.org/W2139608682","https://openalex.org/W2143211346","https://openalex.org/W2145388307","https://openalex.org/W2161723275","https://openalex.org/W2162481448","https://openalex.org/W2164281374","https://openalex.org/W2164960226","https://openalex.org/W2170726034","https://openalex.org/W2215622313","https://openalex.org/W4300874750"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W1482209366","https://openalex.org/W3151146928"],"abstract_inverted_index":{"Discriminative":[0],"subgraphs":[1,47],"are":[2],"widely":[3],"used":[4],"to":[5,23,62,88,105,120,137,144],"define":[6],"the":[7,72,108,114,139,154],"feature":[8],"space":[9,75],"for":[10,44,48],"graph":[11,15,49,125],"classification":[12,50,161],"in":[13,51,76,158],"large":[14,36,52],"databases.":[16,37,53],"Several":[17],"scalable":[18],"approaches":[19,156],"have":[20,134],"been":[21,135],"proposed":[22],"mine":[24],"discriminative":[25,46,90],"subgraphs.":[26],"However,":[27],"their":[28],"intensive":[29],"computation":[30],"needs":[31],"prevent":[32],"them":[33],"from":[34],"mining":[35,45],"We":[38],"propose":[39],"an":[40,64],"efficient":[41],"method":[42,55],"GAIA":[43,85,142,152],"Our":[54],"employs":[56],"a":[57,77],"novel":[58],"subgraph":[59,66,73,91],"encoding":[60],"approach":[61],"support":[63],"arbitrary":[65],"pattern":[67,74],"exploration":[68],"order":[69],"and":[70,143,163],"explores":[71],"process":[78],"resembling":[79],"biological":[80],"evolution.":[81],"In":[82,113],"this":[83],"manner,":[84],"is":[86],"able":[87],"find":[89],"patterns":[92,128],"much":[93],"faster":[94],"than":[95],"other":[96,149,155],"algorithms.":[97],"Additionally,":[98],"we":[99,116],"take":[100],"advantage":[101],"of":[102,110,141,160],"parallel":[103],"computing":[104],"further":[106],"improve":[107],"quality":[109],"resulting":[111],"patterns.":[112],"end,":[115],"employ":[117],"sequential":[118],"coverage":[119],"generate":[121],"association":[122],"rules":[123],"as":[124],"classifiers":[126],"using":[127],"mined":[129],"by":[130],"GAIA.":[131],"Extensive":[132],"experiments":[133],"performed":[136],"analyze":[138],"performance":[140],"compare":[145],"it":[146],"with":[147],"two":[148],"state-of-the-art":[150],"approaches.":[151],"outperforms":[153],"both":[157],"terms":[159],"accuracy":[162],"runtime":[164],"efficiency.":[165]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":16},{"year":2014,"cited_by_count":11},{"year":2013,"cited_by_count":6},{"year":2012,"cited_by_count":8}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2016-06-24T00:00:00"}
