{"id":"https://openalex.org/W4205313061","doi":"https://doi.org/10.1109/bigdata52589.2021.9672040","title":"Dynamic Embedding-based Methods for Link Prediction in Machine Learning Semantic Network","display_name":"Dynamic Embedding-based Methods for Link Prediction in Machine Learning Semantic Network","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205313061","doi":"https://doi.org/10.1109/bigdata52589.2021.9672040"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9672040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9672040","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","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/A5055567952","display_name":"Harlin Lee","orcid":"https://orcid.org/0000-0001-6128-9942"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Harlin Lee","raw_affiliation_strings":["Department of Mathematics, University of California, Los Angeles, Los Angeles, USA"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of California, Los Angeles, Los Angeles, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039675729","display_name":"Rishi Sonthalia","orcid":"https://orcid.org/0000-0002-0928-392X"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rishi Sonthalia","raw_affiliation_strings":["Department of Mathematics, University of California, Los Angeles, Los Angeles, USA"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of California, Los Angeles, Los Angeles, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007362138","display_name":"Jacob G. Foster","orcid":"https://orcid.org/0000-0003-4942-8326"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jacob G. Foster","raw_affiliation_strings":["Department of Sociology, University of California, Los Angeles, Los Angeles, USA"],"affiliations":[{"raw_affiliation_string":"Department of Sociology, University of California, Los Angeles, Los Angeles, USA","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5055567952"],"corresponding_institution_ids":["https://openalex.org/I161318765"],"apc_list":null,"apc_paid":null,"fwci":1.362,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.81588448,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"5801","last_page":"5808"},"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":0.9991000294685364,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7799614667892456},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7318050861358643},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.5561808943748474},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5119979977607727},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4946083724498749},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.48484814167022705},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4804791510105133},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4658461809158325},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4593643546104431},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4430685341358185},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43593621253967285},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42606037855148315},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.37685781717300415},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3503165543079376}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7799614667892456},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7318050861358643},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.5561808943748474},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5119979977607727},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4946083724498749},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.48484814167022705},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4804791510105133},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4658461809158325},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4593643546104431},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4430685341358185},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43593621253967285},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42606037855148315},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37685781717300415},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3503165543079376},{"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},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9672040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9672040","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320327239","display_name":"Templeton World Charity Foundation","ror":"https://ror.org/00x0z1472"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2007444087","https://openalex.org/W2008620264","https://openalex.org/W2018045523","https://openalex.org/W2026417691","https://openalex.org/W2032618685","https://openalex.org/W2037705937","https://openalex.org/W2123402141","https://openalex.org/W2130145803","https://openalex.org/W2132022337","https://openalex.org/W2325227998","https://openalex.org/W2793071066","https://openalex.org/W2950657507","https://openalex.org/W2962756421","https://openalex.org/W4246649397","https://openalex.org/W4385245566","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2912814903","https://openalex.org/W2123605750","https://openalex.org/W2088740331","https://openalex.org/W3038102983","https://openalex.org/W2950907416","https://openalex.org/W1559483280","https://openalex.org/W2082479932","https://openalex.org/W2932872266"],"abstract_inverted_index":{"This":[0],"paper":[1],"aims":[2],"to":[3,50,56,115],"accelerate":[4],"scientific":[5,30],"discovery":[6],"by":[7],"studying":[8],"link":[9,77],"prediction":[10,78],"in":[11,20,29,98,126],"a":[12,66,86],"semantic":[13],"network.":[14],"The":[15,80],"nodes":[16],"are":[17],"unidentified":[18],"concepts":[19],"machine":[21],"learning,":[22],"and":[23,52],"the":[24,43,62,69,99,104,109,116,120,131],"time-stamped":[25],"edges":[26,97],"indicate":[27],"co-occurrence":[28],"papers.":[31],"Taking":[32],"advantage":[33],"of":[34,93,103,119],"this":[35],"temporal":[36],"information,":[37],"we":[38],"perform":[39],"node":[40,60],"embedding":[41,127],"on":[42,95],"graph":[44],"at":[45],"every":[46],"year":[47],"from":[48],"1994":[49],"2017,":[51],"apply":[53],"two":[54],"methods":[55],"find":[57],"features":[58,106],"for":[59],"pairs:":[61],"first":[63],"method":[64],"uses":[65,71],"transformer,":[67],"while":[68],"other":[70],"distance":[72,125],"metrics":[73],"combined":[74],"with":[75,85],"known":[76],"features.":[79],"latter":[81],"feature":[82],"extraction":[83],"technique":[84],"3-layer":[87],"multi-layer":[88],"perceptron":[89],"achieved":[90],"an":[91],"AUC":[92],"0.902":[94],"predicting":[96],"2020":[100],"graph.":[101],"Inspection":[102],"resulting":[105],"suggests":[107],"that":[108],"model":[110],"does":[111],"indeed":[112],"pay":[113],"attention":[114],"dynamic":[117],"nature":[118],"features,":[121],"e.g.,":[122],"how":[123],"node-pair":[124],"space":[128],"changes":[129],"over":[130],"years.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}