{"id":"https://openalex.org/W2253654881","doi":"https://doi.org/10.1145/2835776.2835815","title":"Scaling up Link Prediction with Ensembles","display_name":"Scaling up Link Prediction with Ensembles","publication_year":2016,"publication_date":"2016-02-04","ids":{"openalex":"https://openalex.org/W2253654881","doi":"https://doi.org/10.1145/2835776.2835815","mag":"2253654881"},"language":"en","primary_location":{"id":"doi:10.1145/2835776.2835815","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2835776.2835815","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","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/A5101763318","display_name":"Liang Duan","orcid":"https://orcid.org/0000-0001-9473-2533"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liang Duan","raw_affiliation_strings":["SKLSDE Lab, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"SKLSDE Lab, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028089542","display_name":"Char\u0173 C. Aggarwal","orcid":"https://orcid.org/0000-0003-2579-7581"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Charu Aggarwal","raw_affiliation_strings":["IBM T. J. Watson Research Center, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, New York, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006980420","display_name":"Shuai Ma","orcid":"https://orcid.org/0000-0002-4050-0443"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Ma","raw_affiliation_strings":["SKLSDE Lab, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"SKLSDE Lab, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103128873","display_name":"Renjun Hu","orcid":"https://orcid.org/0000-0002-1094-6890"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renjun Hu","raw_affiliation_strings":["SKLSDE Lab, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"SKLSDE Lab, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110207665","display_name":"Jinpeng Huai","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinpeng Huai","raw_affiliation_strings":["SKLSDE Lab, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"SKLSDE Lab, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101763318"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":3.377,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.92452007,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"367","last_page":"376"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9986000061035156,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9901000261306763,"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/computer-science","display_name":"Computer science","score":0.7795184254646301},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7078535556793213},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.7034814357757568},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.6131064891815186},{"id":"https://openalex.org/keywords/factor","display_name":"Factor (programming language)","score":0.5120952129364014},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.4767763912677765},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4684940278530121},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.46278172731399536},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43213313817977905},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.408674955368042},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23489153385162354},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12043029069900513}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7795184254646301},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7078535556793213},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7034814357757568},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.6131064891815186},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.5120952129364014},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4767763912677765},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4684940278530121},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.46278172731399536},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43213313817977905},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.408674955368042},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23489153385162354},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12043029069900513},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2835776.2835815","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2835776.2835815","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/17"}],"awards":[{"id":"https://openalex.org/G3274943203","display_name":null,"funder_award_id":"61322207","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W34646664","https://openalex.org/W188608978","https://openalex.org/W1487588218","https://openalex.org/W1533841329","https://openalex.org/W1755289444","https://openalex.org/W1902027874","https://openalex.org/W1964731129","https://openalex.org/W1972719705","https://openalex.org/W1979104937","https://openalex.org/W2003707464","https://openalex.org/W2017102965","https://openalex.org/W2018156309","https://openalex.org/W2022867359","https://openalex.org/W2026417691","https://openalex.org/W2028020328","https://openalex.org/W2037933327","https://openalex.org/W2067825627","https://openalex.org/W2071018679","https://openalex.org/W2088571412","https://openalex.org/W2095054612","https://openalex.org/W2100026763","https://openalex.org/W2101108259","https://openalex.org/W2106545428","https://openalex.org/W2107569009","https://openalex.org/W2109480754","https://openalex.org/W2117234597","https://openalex.org/W2118947057","https://openalex.org/W2127345773","https://openalex.org/W2133075925","https://openalex.org/W2136897707","https://openalex.org/W2139694940","https://openalex.org/W2148847267","https://openalex.org/W2151078464","https://openalex.org/W2153622543","https://openalex.org/W2154454189","https://openalex.org/W2160664735","https://openalex.org/W2161902954","https://openalex.org/W2171960770","https://openalex.org/W2294878478","https://openalex.org/W2364964713","https://openalex.org/W2571268788","https://openalex.org/W2912934387","https://openalex.org/W2963316155","https://openalex.org/W3000438703","https://openalex.org/W6679197216","https://openalex.org/W6683242991","https://openalex.org/W6707606025"],"related_works":["https://openalex.org/W2487162673","https://openalex.org/W2793211469","https://openalex.org/W2949152769","https://openalex.org/W4372354731","https://openalex.org/W2807634898","https://openalex.org/W1692008701","https://openalex.org/W2942366970","https://openalex.org/W2597588799","https://openalex.org/W4360593462","https://openalex.org/W2562400057"],"abstract_inverted_index":{"A":[0],"network":[1],"with":[2,151,190],"$n$":[3],"nodes":[4],"contains":[5],"O(n2)":[6],"possible":[7],"links.":[8],"Even":[9],"for":[10,24,81,94],"networks":[11,164],"of":[12,61,90,143,154,165,178,184,203],"modest":[13,166],"size,":[14],"it":[15,47,106],"is":[16,35,48,107,133],"often":[17,49],"difficult":[18,50],"to":[19,38,51,70,109,127,135],"evaluate":[20],"all":[21],"pairwise":[22],"possibilities":[23],"links":[25],"in":[26,176],"a":[27,87,96],"meaningful":[28],"way.":[29],"Furthermore,":[30,168],"even":[31],"though":[32],"link":[33,76,84,130,138],"prediction":[34,77,139],"closely":[36],"related":[37],"missing":[39],"value":[40],"estimation":[41],"problems,":[42],"such":[43,55],"as":[44,56],"collaborative":[45],"filtering,":[46],"use":[52,153],"sophisticated":[53],"models":[54,189],"latent":[57,155,187],"factor":[58,156,188],"methods":[59,78],"because":[60],"their":[62],"computational":[63,72],"complexity":[64],"over":[65,86,99,114,163],"very":[66,193],"large":[67,194],"networks.":[68,102,117,195],"Due":[69],"this":[71,119],"complexity,":[73],"most":[74],"known":[75],"are":[79,148],"designed":[80],"evaluating":[82],"the":[83,100,115,152,169,182,199],"propensity":[85],"specified":[88],"subset":[89],"links,":[91],"rather":[92],"than":[93],"performing":[95],"global":[97],"search":[98,113],"entire":[101,116],"In":[103,118],"practice,":[104],"however,":[105],"essential":[108],"perform":[110],"an":[111,123],"exhaustive":[112],"paper,":[120],"we":[121],"propose":[122],"ensemble":[124,170],"enabled":[125,171],"approach":[126,172],"scaling":[128],"up":[129],"prediction,":[131],"which":[132,158],"able":[134],"decompose":[136],"traditional":[137],"problems":[140],"into":[141],"subproblems":[142,147],"smaller":[144],"size.":[145,167],"These":[146],"each":[149],"solved":[150],"models,":[157],"can":[159],"be":[160],"effectively":[161],"implemented":[162],"has":[173],"several":[174],"advantages":[175],"terms":[177],"performance.":[179],"We":[180],"show":[181],"advantage":[183],"using":[185],"ensemble-based":[186],"experiments":[191],"on":[192],"Experimental":[196],"results":[197],"demonstrate":[198],"effectiveness":[200],"and":[201],"scalability":[202],"our":[204],"approach.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":3}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}
