{"id":"https://openalex.org/W3008054143","doi":"https://doi.org/10.1109/bigdata47090.2019.9006525","title":"G-Finder: Approximate Attributed Subgraph Matching","display_name":"G-Finder: Approximate Attributed Subgraph Matching","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3008054143","doi":"https://doi.org/10.1109/bigdata47090.2019.9006525","mag":"3008054143"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006525","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006525","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big 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/A5100747139","display_name":"Lihui Liu","orcid":"https://orcid.org/0000-0002-3752-038X"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lihui Liu","raw_affiliation_strings":["Department of Computer Science, University of Illinois at Urbana Champaign"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Illinois at Urbana Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007722983","display_name":"Boxin Du","orcid":"https://orcid.org/0000-0002-1300-6140"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Boxin Du","raw_affiliation_strings":["Department of Computer Science, University of Illinois at Urbana Champaign"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Illinois at Urbana Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102267796","display_name":"Jiejun Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I200576644","display_name":"HRL Laboratories (United States)","ror":"https://ror.org/05p7te762","country_code":"US","type":"company","lineage":["https://openalex.org/I200576644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiejun xu","raw_affiliation_strings":["HRL Laboratories LLC"],"affiliations":[{"raw_affiliation_string":"HRL Laboratories LLC","institution_ids":["https://openalex.org/I200576644"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068043486","display_name":"Hanghang Tong","orcid":"https://orcid.org/0000-0003-4405-3887"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hanghang Tong","raw_affiliation_strings":["Department of Computer Science, University of Illinois at Urbana Champaign"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Illinois at Urbana Champaign","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100747139"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":1.8381,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":{"value":0.89009476,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"513","last_page":"522"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9991999864578247,"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/T12326","display_name":"Network Packet Processing and Optimization","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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":0.7204921245574951},{"id":"https://openalex.org/keywords/generality","display_name":"Generality","score":0.6402631998062134},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5824942588806152},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5244731903076172},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.46753546595573425},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4073773920536041},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4004947245121002},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35536038875579834},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21361789107322693}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7204921245574951},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.6402631998062134},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5824942588806152},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5244731903076172},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.46753546595573425},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4073773920536041},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4004947245121002},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35536038875579834},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21361789107322693},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006525","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006525","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1509240356","https://openalex.org/W1975068437","https://openalex.org/W1996551363","https://openalex.org/W2047944694","https://openalex.org/W2048653843","https://openalex.org/W2073570452","https://openalex.org/W2078789330","https://openalex.org/W2083235381","https://openalex.org/W2110034858","https://openalex.org/W2112200469","https://openalex.org/W2112776329","https://openalex.org/W2122306963","https://openalex.org/W2140840007","https://openalex.org/W2142498761","https://openalex.org/W2142965177","https://openalex.org/W2143363350","https://openalex.org/W2147913014","https://openalex.org/W2154878346","https://openalex.org/W2254833717","https://openalex.org/W2387462954","https://openalex.org/W2399101711","https://openalex.org/W2423652555","https://openalex.org/W2744353700","https://openalex.org/W2798875768","https://openalex.org/W2913192763","https://openalex.org/W2948742909","https://openalex.org/W2962756421","https://openalex.org/W6681029592"],"related_works":["https://openalex.org/W2045049461","https://openalex.org/W4381094582","https://openalex.org/W1978893398","https://openalex.org/W1977906818","https://openalex.org/W2369625323","https://openalex.org/W2364579609","https://openalex.org/W1522139108","https://openalex.org/W2353528968","https://openalex.org/W2032776242","https://openalex.org/W2115989734"],"abstract_inverted_index":{"Subgraph":[0],"matching":[1,37,68,137],"is":[2,29],"a":[3,7,82,91,104],"core":[4],"primitive":[5],"across":[6],"number":[8],"of":[9,26,73,135,175],"disciplines,":[10],"ranging":[11],"from":[12],"data":[13,50,85,177],"mining,":[14],"databases,":[15],"information":[16],"retrieval,":[17],"computer":[18],"vision":[19],"to":[20,33,63,95,110,131,158],"natural":[21],"language":[22],"processing.":[23],"Despite":[24],"decades":[25],"efforts,":[27],"it":[28],"still":[30],"highly":[31],"challenging":[32],"balance":[34],"between":[35],"the":[36,40,45,49,65,71,74,112,163,173,176,182],"accuracy":[38],"and":[39,97,102,107,167],"computational":[41],"efficiency,":[42,169],"especially":[43],"when":[44],"query":[46,183],"graph":[47,51,178],"and/or":[48,148],"are":[52,77],"large.":[53],"In":[54],"this":[55],"paper,":[56],"we":[57],"propose":[58],"an":[59,116],"index-based":[60],"algorithm":[61,76],"(G-FINDER)":[62],"find":[64],"top-k":[66],"approximate":[67],"subgraphs.":[69],"At":[70],"heart":[72],"proposed":[75,120],"two":[78],"techniques,":[79],"including":[80,126],"(1)":[81,127],"novel":[83],"auxiliary":[84],"structure":[86],"(LOOKUP-TABLE)":[87],"in":[88],"conjunction":[89],"with":[90],"neighborhood":[92],"expansion":[93],"method":[94],"effectively":[96],"efficiently":[98],"index":[99],"candidate":[100],"vertices,":[101],"(2)":[103,154],"dynamic":[105],"filtering":[106],"refinement":[108],"strategy":[109],"prune":[111],"false":[113],"candidates":[114],"at":[115],"early":[117],"stage.":[118],"The":[119],"G-FINDER":[121],"bears":[122],"some":[123],"distinctive":[124],"features,":[125],"generality,":[128],"being":[129],"able":[130],"handle":[132],"different":[133],"types":[134],"inexact":[136],"(e.g.,":[138],"missing":[139,141],"nodes,":[140],"edges,":[142],"intermediate":[143],"vertices)":[144],"on":[145],"node":[146],"attributed":[147,150],"edge":[149],"graphs":[151],"or":[152],"multigraphs;":[153],"effectiveness,":[155],"achieving":[156],"up":[157],"30%":[159],"Fl-Score":[160],"improvement":[161],"over":[162],"best":[164],"known":[165],"competitor;":[166],"(3)":[168],"scaling":[170],"near-linearly":[171],"w.r.t.":[172],"size":[174],"as":[179,181],"well":[180],"graph.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":3}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}
