{"id":"https://openalex.org/W4320559227","doi":"https://doi.org/10.1145/3543507.3583544","title":"Toward Degree Bias in Embedding-Based Knowledge Graph Completion","display_name":"Toward Degree Bias in Embedding-Based Knowledge Graph Completion","publication_year":2023,"publication_date":"2023-04-26","ids":{"openalex":"https://openalex.org/W4320559227","doi":"https://doi.org/10.1145/3543507.3583544"},"language":"en","primary_location":{"id":"doi:10.1145/3543507.3583544","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3543507.3583544","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2023","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2302.05044","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057319756","display_name":"Harry Shomer","orcid":"https://orcid.org/0000-0001-5081-1870"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Harry Shomer","raw_affiliation_strings":["Computer Science, Michigan State University, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science, Michigan State University, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100758371","display_name":"Wei Jin","orcid":"https://orcid.org/0000-0002-5054-954X"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Jin","raw_affiliation_strings":["Computer Science, Michigan State University, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science, Michigan State University, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025401204","display_name":"Wentao Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wentao Wang","raw_affiliation_strings":["Computer Science, Michigan State University, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science, Michigan State University, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040639891","display_name":"Jiliang Tang","orcid":"https://orcid.org/0000-0001-7125-3898"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiliang Tang","raw_affiliation_strings":["Computer Science, Michigan State University, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science, Michigan State University, USA","institution_ids":["https://openalex.org/I87216513"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5057319756"],"corresponding_institution_ids":["https://openalex.org/I87216513"],"apc_list":null,"apc_paid":null,"fwci":3.8272,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.94549054,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"705","last_page":"715"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9854000210762024,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9764000177383423,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7733173370361328},{"id":"https://openalex.org/keywords/degree","display_name":"Degree (music)","score":0.714982271194458},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6049147844314575},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5590572953224182},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4926057457923889},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4666564166545868},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4485182762145996},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.4421728253364563},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34782350063323975},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33984947204589844}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7733173370361328},{"id":"https://openalex.org/C2775997480","wikidata":"https://www.wikidata.org/wiki/Q586277","display_name":"Degree (music)","level":2,"score":0.714982271194458},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6049147844314575},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5590572953224182},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4926057457923889},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4666564166545868},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4485182762145996},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.4421728253364563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34782350063323975},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33984947204589844},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3543507.3583544","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3543507.3583544","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2023","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2302.05044","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.05044","pdf_url":"https://arxiv.org/pdf/2302.05044","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2302.05044","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.05044","pdf_url":"https://arxiv.org/pdf/2302.05044","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","display_name":"No poverty","score":0.6000000238418579}],"awards":[{"id":"https://openalex.org/G1432373144","display_name":null,"funder_award_id":"W911NF-21-1-","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G3371284211","display_name":null,"funder_award_id":"IIS2212032","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3412060240","display_name":"CAREER: Real-World Networks: Modeling and Analysis of Signed Networks with Positive and Negative Links","funder_award_id":"1845081","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3565905726","display_name":"SaTC: CORE: Small: Side-channel Attacks Against Mobile Users: Singularity Detection, Behavior Identification, and Automated Rectification","funder_award_id":"1815636","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3695851104","display_name":"III:Medium:Computation and Communication Efficient Distributed Learning","funder_award_id":"2212032","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G434729842","display_name":null,"funder_award_id":"1928278","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G545420438","display_name":"Collaborative Research: III: Medium: Graph Neural Networks for Heterophilous Data: Advancing the Theory, Models, and Applications","funder_award_id":"2212144","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"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/G6113845086","display_name":null,"funder_award_id":"2035472","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7402679956","display_name":null,"funder_award_id":"IOS2035472","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"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/G7710557890","display_name":null,"funder_award_id":"IIS1845081","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7794432752","display_name":null,"funder_award_id":"IIS2212144","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8316610220","display_name":"III: Medium: Collaborative Research: Towards Scalable and Interpretable Graph Neural Networks","funder_award_id":"1955285","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"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/G8657258523","display_name":"TRTech-PGR: Connecting sequences to functions within and between species through computational modeling and experimental studies","funder_award_id":"2107215","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G877152271","display_name":null,"funder_award_id":"W911NF-21-1-0198","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"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/F4320307791","display_name":"Cisco Systems","ror":"https://ror.org/03yt1ez60"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4320559227.pdf","grobid_xml":"https://content.openalex.org/works/W4320559227.grobid-xml"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W205829674","https://openalex.org/W1522301498","https://openalex.org/W1533230146","https://openalex.org/W1976790167","https://openalex.org/W2127795553","https://openalex.org/W2148143831","https://openalex.org/W2250184916","https://openalex.org/W2604314403","https://openalex.org/W2626967530","https://openalex.org/W2728059831","https://openalex.org/W2751465153","https://openalex.org/W2765407302","https://openalex.org/W2884561390","https://openalex.org/W2911778742","https://openalex.org/W2949182780","https://openalex.org/W2962886429","https://openalex.org/W2963173418","https://openalex.org/W2963351448","https://openalex.org/W2964015378","https://openalex.org/W2966787092","https://openalex.org/W2970121940","https://openalex.org/W2998385486","https://openalex.org/W3003897916","https://openalex.org/W3034601242","https://openalex.org/W3035552357","https://openalex.org/W3035739162","https://openalex.org/W3044057088","https://openalex.org/W3048692209","https://openalex.org/W3090829264","https://openalex.org/W3099836348","https://openalex.org/W3103296573","https://openalex.org/W3103409210","https://openalex.org/W3108105109","https://openalex.org/W3120491054","https://openalex.org/W3125645205","https://openalex.org/W3126272270","https://openalex.org/W3134509497","https://openalex.org/W3136640655","https://openalex.org/W3165146808","https://openalex.org/W3167673482","https://openalex.org/W3172133997","https://openalex.org/W3172525852","https://openalex.org/W3203679094","https://openalex.org/W3207813134","https://openalex.org/W4221150228","https://openalex.org/W4226148503","https://openalex.org/W4281561077","https://openalex.org/W4286903517","https://openalex.org/W4287325357","https://openalex.org/W4287685533","https://openalex.org/W4295312788","https://openalex.org/W4300088958","https://openalex.org/W4301359866","https://openalex.org/W4307330258","https://openalex.org/W6745136726","https://openalex.org/W6804000844"],"related_works":["https://openalex.org/W2604454537","https://openalex.org/W2808284704","https://openalex.org/W2897702399","https://openalex.org/W4206028705","https://openalex.org/W2757431232","https://openalex.org/W2954554213","https://openalex.org/W2251363251","https://openalex.org/W4206547516","https://openalex.org/W4293236197","https://openalex.org/W2883748392"],"abstract_inverted_index":{"A":[0,30],"fundamental":[1],"task":[2],"for":[3,21,51,74],"knowledge":[4,8],"graphs":[5,38],"(KGs)":[6],"is":[7,39],"graph":[9,45],"completion":[10],"(KGC).":[11],"It":[12,42],"aims":[13],"to":[14,56,104,116,120],"predict":[15],"unseen":[16],"edges":[17],"by":[18,47],"learning":[19,34,48],"representations":[20,35,50],"all":[22],"the":[23,82,91,101,142],"entities":[24],"and":[25,77,99,137],"relations":[26],"in":[27,96],"a":[28,110],"KG.":[29],"common":[31],"concern":[32],"when":[33],"on":[36,59,68,145],"traditional":[37],"degree":[40,72,94,105],"bias.":[41,106,123],"can":[43,131],"affect":[44],"algorithms":[46],"poor":[49],"lower-degree":[52],"nodes,":[53],"often":[54],"leading":[55],"low":[57],"performance":[58,83],"such":[60,79,122],"nodes.":[61],"However,":[62],"there":[63,70],"has":[64],"been":[65],"limited":[66],"research":[67],"whether":[69],"exists":[71],"bias":[73,80,95,143],"embedding-based":[75,97,134],"KGC":[76,98,135],"how":[78],"affects":[81],"of":[84,93],"KGC.":[85],"In":[86],"this":[87],"paper,":[88],"we":[89],"validate":[90],"existence":[92],"identify":[100],"key":[102],"factor":[103],"We":[107],"then":[108],"introduce":[109],"novel":[111],"data":[112],"augmentation":[113],"method,":[114],"KG-Mixup,":[115],"generate":[117],"synthetic":[118],"triples":[119],"mitigate":[121],"Extensive":[124],"experiments":[125],"have":[126],"demonstrated":[127],"that":[128],"our":[129],"method":[130],"improve":[132],"various":[133],"methods":[136,140],"outperform":[138],"other":[139],"tackling":[141],"problem":[144],"multiple":[146],"benchmark":[147],"datasets.":[148],"1":[149]},"counts_by_year":[{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":4}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}