{"id":"https://openalex.org/W4399695289","doi":"https://doi.org/10.1145/3711896.3737434","title":"Towards Understanding Link Predictor Generalizability Under Distribution Shifts","display_name":"Towards Understanding Link Predictor Generalizability Under Distribution Shifts","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4399695289","doi":"https://doi.org/10.1145/3711896.3737434"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3737434","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737434","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737434","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737434","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5099137280","display_name":"Jay Revolinsky","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":true,"raw_author_name":"Jay Revolinsky","raw_affiliation_strings":["Michigan State University, East Lansing, USA"],"affiliations":[{"raw_affiliation_string":"Michigan State University, East Lansing, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","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":false,"raw_author_name":"Harry Shomer","raw_affiliation_strings":["Michigan State University, East Lansing, USA"],"affiliations":[{"raw_affiliation_string":"Michigan State University, East Lansing, 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":["Michigan State University, East Lansing, USA"],"affiliations":[{"raw_affiliation_string":"Michigan State University, East Lansing, USA","institution_ids":["https://openalex.org/I87216513"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5099137280"],"corresponding_institution_ids":["https://openalex.org/I87216513"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00049794,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5766","last_page":"5776"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9467999935150146,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9467999935150146,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9279999732971191,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9265000224113464,"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/generalizability-theory","display_name":"Generalizability theory","score":0.9111884832382202},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.8163139820098877},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.44401687383651733},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4176235496997833},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4002628028392792},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.34426063299179077},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.34388530254364014},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.1578567922115326}],"concepts":[{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.9111884832382202},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.8163139820098877},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.44401687383651733},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4176235496997833},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4002628028392792},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.34426063299179077},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34388530254364014},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.1578567922115326},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3711896.3737434","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737434","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737434","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2406.08788","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2406.08788","pdf_url":"https://arxiv.org/pdf/2406.08788","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"},{"id":"doi:10.48550/arxiv.2406.08788","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2406.08788","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1145/3711896.3737434","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737434","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737434","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1032364338","display_name":"Interactive, Individualized Professional Learning for Elementary School Teachers: Enhancing Content and Pedagogical Content Knowledge as a Basis for Improving Practice","funder_award_id":"2405483","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1128133788","display_name":null,"funder_award_id":"Gates","funder_id":"https://openalex.org/F4320306137","funder_display_name":"Bill and Melinda Gates Foundation"},{"id":"https://openalex.org/G1449464469","display_name":null,"funder_award_id":"DUE2234015","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2652453830","display_name":"NeTS: Small: Exploring the Non-Standardized Polices, Operations, and Requirements for 5G Cellular Networks and Beyond: Advancing the Modeling, Tools, and Evaluation","funder_award_id":"2321416","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"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/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/G4659635499","display_name":"Collaborative Research: SaTC: CORE: Medium: Safeguarding Next-Generation Emergency Services (NG-9-1-1) over Cellular Networks: From Design to Practice","funder_award_id":"2246050","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5349860205","display_name":"Intelligent, Adaptive Program with Just-in-time Feedback for Preservice Teachers","funder_award_id":"2234015","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/G5657708766","display_name":null,"funder_award_id":"CNS2321416","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5808857907","display_name":null,"funder_award_id":"DRL2405483","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/G647087074","display_name":null,"funder_award_id":"IOS2107215","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/G7794432752","display_name":null,"funder_award_id":"IIS2212144","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8083687766","display_name":null,"funder_award_id":"CNS2246050","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"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306111","display_name":"U.S. Department of Commerce","ror":"https://ror.org/04chq2495"},{"id":"https://openalex.org/F4320306137","display_name":"Bill and Melinda Gates Foundation","ror":"https://ror.org/0456r8d26"},{"id":"https://openalex.org/F4320308150","display_name":"Michigan Department of Agriculture and Rural Development","ror":"https://ror.org/023cd6641"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4399695289.pdf","grobid_xml":"https://content.openalex.org/works/W4399695289.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W2007444087","https://openalex.org/W2008620264","https://openalex.org/W2124637492","https://openalex.org/W2125101937","https://openalex.org/W2184957013","https://openalex.org/W2420733993","https://openalex.org/W2914721378","https://openalex.org/W2950742983","https://openalex.org/W2964288524","https://openalex.org/W3173148352","https://openalex.org/W3195040486","https://openalex.org/W3196927126","https://openalex.org/W4321485229","https://openalex.org/W4379261299","https://openalex.org/W4385562584","https://openalex.org/W4396735935","https://openalex.org/W4401863661"],"related_works":["https://openalex.org/W2118717649","https://openalex.org/W2413243053","https://openalex.org/W410723623","https://openalex.org/W2015341305","https://openalex.org/W2035068594","https://openalex.org/W4225593417","https://openalex.org/W2573498121","https://openalex.org/W3022298670","https://openalex.org/W3160494304","https://openalex.org/W3006162251"],"abstract_inverted_index":{"State-of-the-art":[0],"link":[1],"prediction":[2],"(LP)":[3],"models":[4,107],"demonstrate":[5],"impressive":[6],"benchmark":[7,11],"results.":[8],"However,":[9],"popular":[10],"datasets":[12],"often":[13,35],"assume":[14],"that":[15],"training,":[16],"validation,":[17],"and":[18,66],"testing":[19],"samples":[20,42],"are":[21],"represen-":[22],"tative":[23],"of":[24,56,104,112,137],"the":[25,54,135],"overall":[26],"dataset":[27,41,61,114],"distribution.":[28],"In":[29],"real-world":[30],"situations,":[31],"this":[32,76],"assumption":[33],"is":[34,144],"incorrect;":[36],"uncontrolled":[37],"factors":[38],"lead":[39],"new":[40],"to":[43,91,121],"come":[44],"from":[45],"a":[46,80,93,131],"different":[47],"distribution":[48,95],"than":[49],"train-":[50],"ing":[51,71],"samples.":[52],"Additionally,":[53],"majority":[55],"recent":[57],"work":[58],"with":[59,116],"graph":[60,128],"shift":[62],"focuses":[63],"on":[64,108,134],"node-":[65],"graph-level":[67],"tasks,":[68],"largely":[69],"ignor-":[70],"link-level":[72],"tasks.":[73],"To":[74],"bridge":[75],"gap,":[77],"we":[78],"introduce":[79],"novel":[81],"splitting":[82],"strategy,":[83],"known":[84],"as":[85],"LPShift,":[86],"which":[87],"utilizes":[88],"structural":[89],"properties":[90],"induce":[92],"controlled":[94],"shift.":[96],"We":[97],"verify":[98],"LPShift's":[99],"effect":[100],"through":[101],"empirical":[102],"evaluation":[103],"SOTA":[105],"LP":[106],"16":[109],"LPShift":[110],"variants":[111],"original":[113],"splits,":[115],"results":[117],"indicating":[118],"drastic":[119],"changes":[120],"model":[122],"performance.":[123],"Additional":[124],"experiments":[125],"demon-":[126],"strate":[127],"structure":[129],"has":[130],"strong":[132],"influence":[133],"success":[136],"current":[138],"generalization":[139],"methods.":[140],"The":[141],"source":[142],"code":[143],"available":[145],"here":[146],"https://github.com/revolins/LPShift.":[147]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2024-06-15T00:00:00"}