{"id":"https://openalex.org/W2360309522","doi":"https://doi.org/10.1145/2939672.2939768","title":"QUINT","display_name":"QUINT","publication_year":2016,"publication_date":"2016-08-08","ids":{"openalex":"https://openalex.org/W2360309522","doi":"https://doi.org/10.1145/2939672.2939768","mag":"2360309522"},"language":"en","primary_location":{"id":"doi:10.1145/2939672.2939768","is_oa":true,"landing_page_url":"https://doi.org/10.1145/2939672.2939768","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=2939768&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"http://dl.acm.org/ft_gateway.cfm?id=2939768&type=pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014174145","display_name":"Liangyue Li","orcid":"https://orcid.org/0000-0001-7630-8851"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Liangyue Li","raw_affiliation_strings":["Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068643894","display_name":"Yuan Yao","orcid":"https://orcid.org/0000-0002-6913-6542"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Yao","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044791875","display_name":"Jie Tang","orcid":"https://orcid.org/0000-0003-3487-4593"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Tang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023696543","display_name":"Wei Fan","orcid":"https://orcid.org/0000-0002-7415-0496"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Fan","raw_affiliation_strings":["Baidu Big Data Lab, Sunnyvale, CA, USA"],"affiliations":[{"raw_affiliation_string":"Baidu Big Data Lab, Sunnyvale, CA, USA","institution_ids":[]}]},{"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/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hanghang Tong","raw_affiliation_strings":["Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5014174145"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":2.3624,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.89677165,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"985","last_page":"994"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11478","display_name":"Caching and Content Delivery","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.993399977684021,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7150063514709473},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.5944735407829285},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5659306645393372},{"id":"https://openalex.org/keywords/network-topology","display_name":"Network topology","score":0.533847451210022},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5133671164512634},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4675995707511902},{"id":"https://openalex.org/keywords/linear-programming","display_name":"Linear programming","score":0.4647841453552246},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.4433090388774872},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.432531476020813},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.42082899808883667},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.38722294569015503},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36381882429122925},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.26393961906433105},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2053486406803131},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18275076150894165}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7150063514709473},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5944735407829285},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5659306645393372},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.533847451210022},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5133671164512634},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4675995707511902},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.4647841453552246},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.4433090388774872},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.432531476020813},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.42082899808883667},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.38722294569015503},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36381882429122925},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26393961906433105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2053486406803131},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18275076150894165},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2939672.2939768","is_oa":true,"landing_page_url":"https://doi.org/10.1145/2939672.2939768","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=2939768&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/2939672.2939768","is_oa":true,"landing_page_url":"https://doi.org/10.1145/2939672.2939768","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=2939768&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6299999952316284,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1231421488","display_name":null,"funder_award_id":"under","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2443676066","display_name":null,"funder_award_id":"W911NF-16-1-0168","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G303484067","display_name":null,"funder_award_id":"IIS1017415","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3366966419","display_name":null,"funder_award_id":"W911NF-16-1","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G37568934","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4243412236","display_name":null,"funder_award_id":"863 Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5511481212","display_name":null,"funder_award_id":"HDTRA1-16-0017","funder_id":"https://openalex.org/F4320332186","funder_display_name":"Defense Threat Reduction Agency"},{"id":"https://openalex.org/G5921281487","display_name":null,"funder_award_id":"number","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6541015203","display_name":null,"funder_award_id":"HDTRA1","funder_id":"https://openalex.org/F4320332186","funder_display_name":"Defense Threat Reduction Agency"},{"id":"https://openalex.org/G657448715","display_name":null,"funder_award_id":"W911NF-16-1-","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G6655563392","display_name":"III: Small: Influence and Virus Propagation in Large Graphs - Theory and Algorithms","funder_award_id":"1017415","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6686995610","display_name":null,"funder_award_id":"2014A","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6773350834","display_name":null,"funder_award_id":"R01LM011986","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G7402129869","display_name":null,"funder_award_id":"LM011986","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7726157001","display_name":null,"funder_award_id":"Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G868521458","display_name":null,"funder_award_id":"IIS1017415","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8865443272","display_name":null,"funder_award_id":"2014AA015103","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8998121839","display_name":null,"funder_award_id":"911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320318547","display_name":"Baidu","ror":"https://ror.org/03vs3wt56"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320332186","display_name":"Defense Threat Reduction Agency","ror":"https://ror.org/04tz64554"},{"id":"https://openalex.org/F4320335773","display_name":"National High-tech Research and Development Program","ror":null},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2360309522.pdf","grobid_xml":"https://content.openalex.org/works/W2360309522.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W1595449516","https://openalex.org/W1784780955","https://openalex.org/W1856231595","https://openalex.org/W1978030011","https://openalex.org/W1981760490","https://openalex.org/W1994389483","https://openalex.org/W1994558922","https://openalex.org/W1997219719","https://openalex.org/W2003707464","https://openalex.org/W2011597392","https://openalex.org/W2021948263","https://openalex.org/W2048653843","https://openalex.org/W2055043387","https://openalex.org/W2063049279","https://openalex.org/W2066432005","https://openalex.org/W2067825627","https://openalex.org/W2083598336","https://openalex.org/W2089750292","https://openalex.org/W2092912084","https://openalex.org/W2094215927","https://openalex.org/W2101409192","https://openalex.org/W2106005123","https://openalex.org/W2107569009","https://openalex.org/W2107933610","https://openalex.org/W2109865732","https://openalex.org/W2110325612","https://openalex.org/W2127607093","https://openalex.org/W2128119682","https://openalex.org/W2129376520","https://openalex.org/W2129957127","https://openalex.org/W2131807809","https://openalex.org/W2133299088","https://openalex.org/W2134435539","https://openalex.org/W2135003210","https://openalex.org/W2146728697","https://openalex.org/W2154454189","https://openalex.org/W2169223901","https://openalex.org/W2171774716","https://openalex.org/W2171810970","https://openalex.org/W2420733993","https://openalex.org/W2595142274","https://openalex.org/W2755088640","https://openalex.org/W2784619191","https://openalex.org/W2952748676","https://openalex.org/W3099765941"],"related_works":["https://openalex.org/W1594844924","https://openalex.org/W2909382770","https://openalex.org/W2143670980","https://openalex.org/W1577931366","https://openalex.org/W2024555427","https://openalex.org/W1532213207","https://openalex.org/W2355215981","https://openalex.org/W2382078898","https://openalex.org/W2608348709","https://openalex.org/W2163309909"],"abstract_inverted_index":{"Measuring":[0],"node":[1,91,137],"proximity":[2,92,138],"on":[3,73,218,238],"large":[4],"scale":[5,246],"networks":[6,89,250],"is":[7,46,109,124,186],"a":[8,48,101,127,173,189,201,219,255,258],"fundamental":[9],"building":[10],"block":[11],"in":[12,82,254],"many":[13],"application":[14],"domains,":[15],"ranging":[16],"from":[17],"computer":[18],"vision,":[19],"e-commerce,":[20],"social":[21],"networks,":[22,225],"software":[23],"engineering,":[24],"disaster":[25],"management":[26],"to":[27,65,86,111,180,188,248],"biology":[28],"and":[29,55,118,251],"epidemiology.":[30],"The":[31],"state":[32],"of":[33,208,222,257],"the":[34,43,51,56,68,74,80,114,119,135,145,157,162,205,209,229,235],"art":[35],"(e.g.,":[36],"random":[37],"walk":[38],"based":[39,72],"methods)":[40],"typically":[41],"assumes":[42],"input":[44,158],"network":[45,53,116,136,184],"given":[47,155,190],"priori,":[49],"with":[50,200],"known":[52],"topology":[54,117],"associated":[57,120],"edge":[58,70,121,131],"weights.":[59,122],"A":[60],"few":[61],"recent":[62],"works":[63,147],"aim":[64],"further":[66],"infer":[67,113,181],"optimal":[69,88,115,151,183],"weights":[71],"side":[75],"information.":[76],"This":[77,123],"paper":[78],"generalizes":[79],"challenge":[81],"multiple":[83],"dimensions,":[84],"aiming":[85],"learn":[87],"for":[90,165],"measures.":[93,139],"First":[94],"(optimization":[95,141,193],"scope),":[96],"our":[97,168,198],"proposed":[98,230],"formulation":[99],"explores":[100],"much":[102,174],"larger":[103],"parameter":[104],"space,":[105],"so":[106],"that":[107,185,228],"it":[108,154],"able":[110,179],"simultaneously":[112],"important":[125],"as":[126,156],"noisy":[128],"or":[129,159],"missing":[130],"could":[132],"greatly":[133],"mislead":[134],"Second":[140],"granularity),":[142],"while":[143],"all":[144,166,239],"existing":[146,236],"assume":[148],"one":[149],"common":[150],"network,":[152],"be":[153],"learned":[160],"by":[161],"algorithms,":[163],"exists":[164],"queries,":[167],"method":[169],"performs":[170],"optimization":[171],"at":[172],"finer":[175],"granularity,":[176],"essentially":[177],"being":[178],"an":[182],"specific":[187],"query.":[191],"Third":[192],"efficiency),":[194],"we":[195],"carefully":[196],"design":[197],"algorithms":[199,231],"linear":[202],"complexity":[203],"wrt":[204],"neighborhood":[206],"size":[207],"user":[210],"preference":[211],"set.":[212],"We":[213],"perform":[214],"extensive":[215],"empirical":[216],"evaluations":[217],"diverse":[220],"set":[221],"10+":[223],"real":[224],"which":[226],"show":[227],"(1)":[232],"consistently":[233],"outperform":[234],"methods":[237],"six":[240],"commonly":[241],"used":[242],"metrics;":[243],"(2)":[244],"empirically":[245],"sub-linearly":[247],"billion-scale":[249],"(3)":[252],"respond":[253],"fraction":[256],"second.":[259]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2016-06-24T00:00:00"}
