{"id":"https://openalex.org/W4387848775","doi":"https://doi.org/10.1145/3583780.3614941","title":"Joint Link Prediction Via Inference from a Model","display_name":"Joint Link Prediction Via Inference from a Model","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387848775","doi":"https://doi.org/10.1145/3583780.3614941"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3614941","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3614941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://vbn.aau.dk/files/755188560/full1409-naddaf.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040167886","display_name":"Parmis Naddaf","orcid":"https://orcid.org/0000-0002-5541-5276"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Parmis Naddaf","raw_affiliation_strings":["Simon Fraser University, Burnaby, BC, Canada"],"affiliations":[{"raw_affiliation_string":"Simon Fraser University, Burnaby, BC, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093106439","display_name":"Erfaneh Mahmoudzaheh Ahmadi Nejad","orcid":"https://orcid.org/0000-0001-8940-6020"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Erfaneh Mahmoudzaheh Ahmadi Nejad","raw_affiliation_strings":["Simon Fraser University, Burnaby, BC, Canada"],"affiliations":[{"raw_affiliation_string":"Simon Fraser University, Burnaby, BC, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029304681","display_name":"Kiarash Zahirnia","orcid":"https://orcid.org/0009-0001-2594-0619"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Kiarash Zahirnia","raw_affiliation_strings":["Simon Fraser University, Burnaby, BC, Canada"],"affiliations":[{"raw_affiliation_string":"Simon Fraser University, Burnaby, BC, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014289413","display_name":"Manfred Jaeger","orcid":"https://orcid.org/0000-0002-5641-8153"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Manfred Jaeger","raw_affiliation_strings":["Aalborg University, Aalborg, Denmark"],"affiliations":[{"raw_affiliation_string":"Aalborg University, Aalborg, Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026297632","display_name":"Oliver Schulte","orcid":"https://orcid.org/0000-0002-2805-4313"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Oliver Schulte","raw_affiliation_strings":["Simon Fraser University, Burnaby, BC, Canada"],"affiliations":[{"raw_affiliation_string":"Simon Fraser University, Burnaby, BC, Canada","institution_ids":["https://openalex.org/I18014758"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5040167886"],"corresponding_institution_ids":["https://openalex.org/I18014758"],"apc_list":null,"apc_paid":null,"fwci":0.1737,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57756253,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1877","last_page":"1886"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.998199999332428,"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/T11719","display_name":"Data Quality and Management","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.778798520565033},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7649174928665161},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.583272397518158},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5658395290374756},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.563984751701355},{"id":"https://openalex.org/keywords/predictive-inference","display_name":"Predictive inference","score":0.559764564037323},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5483234524726868},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.5303972959518433},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5279017090797424},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.5127373933792114},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5069201588630676},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47054818272590637},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.470287561416626},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4441162049770355},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4200217127799988},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.22838634252548218},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.21405649185180664},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.1398771107196808},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.1389765739440918},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12468031048774719},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08002403378486633}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.778798520565033},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7649174928665161},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.583272397518158},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5658395290374756},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.563984751701355},{"id":"https://openalex.org/C917703","wikidata":"https://www.wikidata.org/wiki/Q7239668","display_name":"Predictive inference","level":5,"score":0.559764564037323},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5483234524726868},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.5303972959518433},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5279017090797424},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.5127373933792114},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5069201588630676},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47054818272590637},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.470287561416626},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4441162049770355},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4200217127799988},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.22838634252548218},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.21405649185180664},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.1398771107196808},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.1389765739440918},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12468031048774719},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08002403378486633},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","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/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3583780.3614941","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3614941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/28af8b8f-1d62-4fa4-ad6f-5d776a00439f","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/28af8b8f-1d62-4fa4-ad6f-5d776a00439f","pdf_url":"https://vbn.aau.dk/files/755188560/full1409-naddaf.pdf","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Naddaf , P , Zahirnia , K , Jaeger , M & Schulte , O 2023 , Joint Link Prediction Via Inference from a Model . in CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management . Association for Computing Machinery , pp. 1877-1886 , 32nd ACM International Conference on Information and Knowledge Management , Birmingham , United Kingdom , 21/10/2023 . https://doi.org/10.1145/3583780.3614941","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:publications/28af8b8f-1d62-4fa4-ad6f-5d776a00439f","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/28af8b8f-1d62-4fa4-ad6f-5d776a00439f","pdf_url":"https://vbn.aau.dk/files/755188560/full1409-naddaf.pdf","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Naddaf , P , Zahirnia , K , Jaeger , M & Schulte , O 2023 , Joint Link Prediction Via Inference from a Model . in CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management . Association for Computing Machinery , pp. 1877-1886 , 32nd ACM International Conference on Information and Knowledge Management , Birmingham , United Kingdom , 21/10/2023 . https://doi.org/10.1145/3583780.3614941","raw_type":"contributionToPeriodical"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6659211675","display_name":null,"funder_award_id":"RGPAS-2018-522721","funder_id":"https://openalex.org/F4320334593","funder_display_name":"Natural Sciences and Engineering Research Council of Canada"}],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4387848775.pdf"},"referenced_works_count":11,"referenced_works":["https://openalex.org/W1976526581","https://openalex.org/W2188365844","https://openalex.org/W2242459625","https://openalex.org/W2243132409","https://openalex.org/W2799059383","https://openalex.org/W2899831870","https://openalex.org/W2912083425","https://openalex.org/W3101485296","https://openalex.org/W3193389962","https://openalex.org/W4206301482","https://openalex.org/W4309495303"],"related_works":["https://openalex.org/W2776613281","https://openalex.org/W3192934159","https://openalex.org/W2225746827","https://openalex.org/W3037690595","https://openalex.org/W2911337295","https://openalex.org/W2119468838","https://openalex.org/W4298105224","https://openalex.org/W4401499842","https://openalex.org/W2501472105","https://openalex.org/W2388631242"],"abstract_inverted_index":{"A":[0],"Joint":[1],"Link":[2],"Prediction":[3],"Query":[4],"(JLPQ)":[5],"specifies":[6],"a":[7,51,58,95,121],"set":[8,16],"of":[9,17,97],"links":[10,18,42],"to":[11,94],"be":[12],"predicted,":[13],"given":[14],"another":[15],"as":[19,21,24],"well":[20,32],"node":[22],"attributes":[23],"evidence.":[25],"While":[26],"single":[27],"link":[28,81,99,116,131],"prediction":[29,100,117,132],"has":[30,44],"been":[31],"studied":[33],"in":[34],"literature":[35],"on":[36,102,139],"deep":[37,60],"graph":[38],"learning,":[39],"predicting":[40],"multiple":[41],"together":[43],"gained":[45],"little":[46],"attention.":[47],"This":[48],"paper":[49],"presents":[50],"novel":[52],"framework":[53],"for":[54,69,83,109],"computing":[55],"JLPQs":[56],"using":[57],"probabilistic":[59],"Graph":[61,74],"Generative":[62],"Model.":[63],"Specifically,":[64],"we":[65,91],"develop":[66],"inference":[67,93,119],"procedures":[68],"an":[70],"inductively":[71],"trained":[72],"Variational":[73],"Auto-Encoder":[75],"(VGAE)":[76],"that":[77,108],"estimates":[78],"the":[79,129,140],"joint":[80,98,115],"probability":[82],"any":[84],"input":[85],"JLPQ,":[86],"without":[87],"retraining.":[88],"For":[89],"evaluation,":[90],"apply":[92],"range":[96],"queries":[101],"six":[103],"benchmark":[104],"datasets.":[105],"We":[106],"find":[107],"most":[110],"datasets":[111],"and":[112],"query":[113],"types,":[114],"via":[118],"from":[120],"model":[122],"achieves":[123],"good":[124],"predictive":[125],"performance,":[126],"better":[127],"than":[128],"independent":[130],"baselines":[133],"(by":[134],"0.02-0.4":[135],"AUC":[136],"points":[137],"depending":[138],"dataset).":[141]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}