{"id":"https://openalex.org/W2585185247","doi":"https://doi.org/10.1145/3018661.3018734","title":"Link Prediction with Cardinality Constraint","display_name":"Link Prediction with Cardinality Constraint","publication_year":2017,"publication_date":"2017-02-02","ids":{"openalex":"https://openalex.org/W2585185247","doi":"https://doi.org/10.1145/3018661.3018734","mag":"2585185247"},"language":"en","primary_location":{"id":"doi:10.1145/3018661.3018734","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3018661.3018734","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3018734&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth ACM International Conference on Web Search 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=3018734&type=pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100462824","display_name":"Jiawei Zhang","orcid":"https://orcid.org/0000-0002-2111-7617"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jiawei Zhang","raw_affiliation_strings":["University of Illinois at Chicago, Chicago, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100765312","display_name":"Jianhui Chen","orcid":"https://orcid.org/0000-0002-7757-6089"},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianhui Chen","raw_affiliation_strings":["Yahoo Research, Sunnyvale, CA, USA"],"affiliations":[{"raw_affiliation_string":"Yahoo Research, Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I4210134091"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052097137","display_name":"Junxing Zhu","orcid":"https://orcid.org/0000-0001-5221-1318"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junxing Zhu","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029392006","display_name":"Yi Chang","orcid":"https://orcid.org/0000-0003-2697-8093"},"institutions":[{"id":"https://openalex.org/I4210146936","display_name":"Huawei Technologies (United States)","ror":"https://ror.org/03jyqk712","country_code":"US","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210146936"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Chang","raw_affiliation_strings":["Huawei Research America, Santa Clara, CA, USA"],"affiliations":[{"raw_affiliation_string":"Huawei Research America, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I4210146936"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036357902","display_name":"Philip S. Yu","orcid":"https://orcid.org/0000-0002-3491-5968"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philip S. Yu","raw_affiliation_strings":["University of Illinois at Chicago, Chicago, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100462824"],"corresponding_institution_ids":["https://openalex.org/I39422238"],"apc_list":null,"apc_paid":null,"fwci":2.9463,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.91019518,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"121","last_page":"130"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9990000128746033,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9965999722480774,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9878000020980835,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cardinality","display_name":"Cardinality (data modeling)","score":0.8315965533256531},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6548880338668823},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5782186388969421},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5737192034721375},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5596398115158081},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.5123194456100464},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.49734142422676086},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.4796493649482727},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4394473433494568},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3225347399711609},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.25808119773864746},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23785868287086487},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.14974883198738098},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.12523868680000305}],"concepts":[{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.8315965533256531},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6548880338668823},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5782186388969421},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5737192034721375},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5596398115158081},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.5123194456100464},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.49734142422676086},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.4796493649482727},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4394473433494568},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3225347399711609},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25808119773864746},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23785868287086487},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.14974883198738098},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.12523868680000305},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3018661.3018734","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3018661.3018734","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3018734&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3018661.3018734","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3018661.3018734","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3018734&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3218167762","display_name":null,"funder_award_id":"IIS-1526499","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7398055925","display_name":null,"funder_award_id":"CNS-1626432","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2585185247.pdf","grobid_xml":"https://content.openalex.org/works/W2585185247.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W188608978","https://openalex.org/W1916698127","https://openalex.org/W1972719705","https://openalex.org/W1996921275","https://openalex.org/W2011039300","https://openalex.org/W2011268914","https://openalex.org/W2022580894","https://openalex.org/W2047532797","https://openalex.org/W2068155033","https://openalex.org/W2074837759","https://openalex.org/W2109480754","https://openalex.org/W2126024570","https://openalex.org/W2140229016","https://openalex.org/W2144192824","https://openalex.org/W2167467982","https://openalex.org/W2171333143","https://openalex.org/W2222512263","https://openalex.org/W2247394048","https://openalex.org/W2268918789","https://openalex.org/W2327315803","https://openalex.org/W2340622084","https://openalex.org/W2420733993","https://openalex.org/W2499358516","https://openalex.org/W2536716774","https://openalex.org/W2536967169","https://openalex.org/W2768375068","https://openalex.org/W2945526390","https://openalex.org/W4241977206"],"related_works":["https://openalex.org/W2002177687","https://openalex.org/W2058438338","https://openalex.org/W2019471580","https://openalex.org/W1518185400","https://openalex.org/W3200586296","https://openalex.org/W4230332972","https://openalex.org/W2941284322","https://openalex.org/W1998033311","https://openalex.org/W4247322236","https://openalex.org/W4224920876"],"abstract_inverted_index":{"Inferring":[0],"the":[1,17,21,62,75,84,94,107,129,134,145,170,180,201,209,229,249,261,269],"links":[2,22,88,197],"among":[3],"entities":[4],"in":[5,165,192,267],"networks":[6],"is":[7,71,97,121,163,190,221],"an":[8,100,148],"important":[9],"research":[10,41],"problem":[11,65,96,102],"for":[12],"various":[13],"disciplines.":[14],"Depending":[15],"on":[16,43,115,172,213,237,255],"specific":[18],"application":[19],"settings,":[20],"to":[23,29,48,60,92,123,133,141,207,228],"be":[24,124,138],"inferred":[25],"are":[26,110],"usually":[27],"subject":[28,132],"different":[30,243],"cardinality":[31,68,108,135,203,246],"constraints,":[32,69,247],"like":[33],"one-to-one,":[34],"one-to-many":[35],"and":[36,128,175,248,263],"many-to-many.":[37],"However,":[38],"most":[39],"existing":[40],"works":[42],"link":[44,63,153,173,187,202],"prediction":[45,64,154],"problems":[46],"fail":[47],"consider":[49],"such":[50],"a":[51,185,216,225],"kind":[52],"of":[53,87,211,219,245,265],"constraint.":[54],"In":[55],"this":[56,166,193],"paper,":[57,167,194],"we":[58],"propose":[59],"study":[61],"with":[66,242],"general":[67],"which":[70,168,195],"formally":[72],"defined":[73],"as":[74,99,112,224],"CLP":[76,95,230,270],"(Cardinality":[77],"Constrained":[78,158],"Link":[79,159],"Prediction)":[80],"problem.":[81,231,271],"By":[82],"minimizing":[83],"projection":[85],"loss":[86],"from":[89],"feature":[90],"vectors":[91],"labels,":[93],"formulated":[98],"optimization":[101,146],"involving":[103],"multiple":[104],"variables,":[105],"where":[106],"constraints":[109,114,136,204],"modeled":[111],"mathematical":[113],"node":[116],"degrees.":[117],"The":[118],"objective":[119],"function":[120],"shown":[122],"not":[125],"jointly":[126],"convex":[127],"optimal":[130],"solution":[131,227],"can":[137,259],"very":[139],"time-consuming":[140],"achieve.":[142],"To":[143,178],"solve":[144],"problem,":[147,184],"iterative":[149],"variable":[150],"updating":[151,174],"based":[152],"framework":[155],"ITERCLIPS":[156,212,220,254,266],"(Iterative":[157],"Prediction":[160],"&":[161],"Selection)":[162],"introduced":[164,191],"involves":[169],"steps":[171],"selection":[176,188],"alternatively.":[177],"overcome":[179],"high":[181],"time":[182],"cost":[183],"greedy":[186],"step":[189],"picks":[196],"greedily":[198],"while":[199],"preserving":[200],"simultaneously.":[205],"Meanwhile,":[206],"ensure":[208],"effectiveness":[210,262],"large-scale":[214],"networks,":[215],"distributed":[217],"implementation":[218],"further":[222],"presented":[223],"scalable":[226],"Extensive":[232],"experiments":[233],"have":[234],"been":[235],"done":[236],"three":[238],"real-world":[239],"network":[240],"datasets":[241,258],"types":[244],"experimental":[250],"results":[251],"achieved":[252],"by":[253],"all":[256],"these":[257],"demonstrate":[260],"advantages":[264],"solving":[268]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":5}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
