{"id":"https://openalex.org/W3011383353","doi":"https://doi.org/10.1109/tsp.2020.3028495","title":"Tensor Graph Convolutional Networks for Multi-Relational and Robust Learning","display_name":"Tensor Graph Convolutional Networks for Multi-Relational and Robust Learning","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3011383353","doi":"https://doi.org/10.1109/tsp.2020.3028495","mag":"3011383353"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2020.3028495","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2020.3028495","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2003.07729","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023028617","display_name":"Vassilis N. Ioannidis","orcid":"https://orcid.org/0000-0002-8367-0733"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vassilis N. Ioannidis","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":"https://orcid.org/0000-0002-8367-0733","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054312245","display_name":"Antonio G. Marqu\u00e9s","orcid":"https://orcid.org/0000-0002-4642-7718"},"institutions":[{"id":"https://openalex.org/I182083151","display_name":"Universidad Rey Juan Carlos","ror":"https://ror.org/01v5cv687","country_code":"ES","type":"education","lineage":["https://openalex.org/I182083151"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Antonio G. Marques","raw_affiliation_strings":["Department of Signal Theory and Communications, King Juan Carlos University, Madrid, Spain"],"raw_orcid":"https://orcid.org/0000-0002-4642-7718","affiliations":[{"raw_affiliation_string":"Department of Signal Theory and Communications, King Juan Carlos University, Madrid, Spain","institution_ids":["https://openalex.org/I182083151"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026758314","display_name":"Georgios B. Giannakis","orcid":"https://orcid.org/0000-0002-0196-0260"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Georgios B. Giannakis","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":"https://orcid.org/0000-0002-0196-0260","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7547,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.93467337,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"68","issue":null,"first_page":"6535","last_page":"6546"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.988099992275238,"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/computer-science","display_name":"Computer science","score":0.6696790456771851},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.6485006809234619},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5890343189239502},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5357159376144409},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36385437846183777}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6696790456771851},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.6485006809234619},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5890343189239502},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5357159376144409},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36385437846183777},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tsp.2020.3028495","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2020.3028495","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2003.07729","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.07729","pdf_url":"https://arxiv.org/pdf/2003.07729","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2003.07729","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.07729","pdf_url":"https://arxiv.org/pdf/2003.07729","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G219950369","display_name":"CyberSEES: Type 2: Collaborative Research: Tenable Power Distribution Networks","funder_award_id":"1442686","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2962050449","display_name":null,"funder_award_id":"171141","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4963119340","display_name":"EAGER-DynamicData:  Judicious Censoring, Random Sketching, and Efficient Validate for Learning Patterns from Dynamically-Changing and Large-Scale Data Sets","funder_award_id":"1500713","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6799522020","display_name":"De hipertensi\u00f3n arterial a diabetes. Identificaci\u00f3n de factores de riesgo mediante aprendizaje autom\u00e1tico con datos heterog\u00e9neos","funder_award_id":"DTS17/00158","funder_id":"https://openalex.org/F4320334923","funder_display_name":"Instituto de Salud Carlos III"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309636","display_name":"University of Minnesota","ror":"https://ror.org/03grvy078"},{"id":"https://openalex.org/F4320334923","display_name":"Instituto de Salud Carlos III","ror":"https://ror.org/00ca2c886"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":74,"referenced_works":["https://openalex.org/W390146837","https://openalex.org/W649146021","https://openalex.org/W950821216","https://openalex.org/W1498436455","https://openalex.org/W1522301498","https://openalex.org/W1542912205","https://openalex.org/W1774304772","https://openalex.org/W1945616565","https://openalex.org/W2006045909","https://openalex.org/W2050322675","https://openalex.org/W2061425021","https://openalex.org/W2061901927","https://openalex.org/W2101491865","https://openalex.org/W2104290444","https://openalex.org/W2110026675","https://openalex.org/W2139823104","https://openalex.org/W2153959628","https://openalex.org/W2194775991","https://openalex.org/W2338990760","https://openalex.org/W2402144811","https://openalex.org/W2407712691","https://openalex.org/W2558748708","https://openalex.org/W2604314403","https://openalex.org/W2610267922","https://openalex.org/W2735272571","https://openalex.org/W2769897290","https://openalex.org/W2796189583","https://openalex.org/W2798585159","https://openalex.org/W2798598284","https://openalex.org/W2803831897","https://openalex.org/W2809251119","https://openalex.org/W2888816596","https://openalex.org/W2900620189","https://openalex.org/W2905110430","https://openalex.org/W2945796017","https://openalex.org/W2953384591","https://openalex.org/W2963207607","https://openalex.org/W2963312446","https://openalex.org/W2963858333","https://openalex.org/W2964015378","https://openalex.org/W2964121744","https://openalex.org/W2964283260","https://openalex.org/W2964321699","https://openalex.org/W2964583308","https://openalex.org/W2964971928","https://openalex.org/W2966149470","https://openalex.org/W2984479486","https://openalex.org/W2995846323","https://openalex.org/W2996268457","https://openalex.org/W2997701990","https://openalex.org/W3001496989","https://openalex.org/W3007598526","https://openalex.org/W3011576596","https://openalex.org/W3013169514","https://openalex.org/W3016062530","https://openalex.org/W3037826211","https://openalex.org/W3087407541","https://openalex.org/W3098276446","https://openalex.org/W3120740533","https://openalex.org/W4206808069","https://openalex.org/W4235019172","https://openalex.org/W4238682518","https://openalex.org/W4239954780","https://openalex.org/W4297733535","https://openalex.org/W6631190155","https://openalex.org/W6640425456","https://openalex.org/W6675747103","https://openalex.org/W6680434193","https://openalex.org/W6699364125","https://openalex.org/W6713134421","https://openalex.org/W6720006811","https://openalex.org/W6751569023","https://openalex.org/W6771291558","https://openalex.org/W6771932116"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2389214306","https://openalex.org/W2382290278","https://openalex.org/W4391913857","https://openalex.org/W2350741829"],"abstract_inverted_index":{"The":[0,93,134],"era":[1],"of":[2,29,52,62,84,117,179,191],"\u201cdata":[3],"deluge\u201d":[4],"has":[5],"sparked":[6],"renewed":[7],"interest":[8],"in":[9,74,141,149,162,168],"graph-based":[10,85],"learning":[11,44,101,170],"methods":[12],"and":[13,20,24,81,90,107,125,159,181,215],"their":[14],"widespread":[15],"applications":[16,142],"ranging":[17],"from":[18,46],"sociology":[19],"biology":[21],"to":[22,71,87,97,128,187,207,217],"transportation":[23],"communications.":[25],"In":[26],"this":[27],"context":[28],"graph-aware":[30],"methods,":[31],"the":[32,63,68,75,82,122,131,144,173,182,192,199],"present":[33],"paper":[34],"introduces":[35],"a":[36,50,58,99,153,163,176],"tensor-graph":[37],"convolutional":[38],"network":[39],"(TGCN)":[40],"for":[41],"scalable":[42],"semi-supervised":[43],"(SSL)":[45],"data":[47,110],"associated":[48],"with":[49,121,211],"collection":[51],"graphs,":[53],"that":[54,198],"are":[55,67,146],"represented":[56],"by":[57],"tensor.":[59],"Key":[60],"aspects":[61],"novel":[64],"TGCN":[65],"architecture":[66,102,136,201],"dynamic":[69],"adaptation":[70],"different":[72,150,183,188],"relations":[73,151,161],"tensor":[76,184],"graph":[77,123,132,155,174],"via":[78],"learnable":[79],"weights,":[80],"consideration":[83],"regularizers":[86],"promote":[88],"smoothness":[89],"alleviate":[91],"over-parameterization.":[92],"ultimate":[94],"goal":[95],"is":[96,137],"design":[98],"powerful":[100],"able":[103],"to:":[104],"discover":[105],"complex":[106],"highly":[108],"nonlinear":[109],"associations,":[111],"combine":[112],"(and":[113],"select)":[114],"multiple":[115],"types":[116],"relations,":[118],"scale":[119],"gracefully":[120],"size,":[124],"remain":[126],"robust":[127,169],"perturbations":[129],"on":[130],"edges.":[133],"proposed":[135,200],"relevant":[138],"not":[139],"only":[140],"where":[143,172],"nodes":[145],"naturally":[147],"involved":[148],"(e.g.,":[152],"multi-relational":[154],"capturing":[156],"family,":[157],"friendship":[158],"work":[160],"social":[164],"network),":[165],"but":[166],"also":[167],"setups":[171],"entails":[175],"certain":[177],"level":[178],"uncertainty,":[180],"slabs":[185],"correspond":[186],"versions":[189],"(realizations)":[190],"nominal":[193],"graph.":[194],"Numerical":[195],"tests":[196],"showcase":[197],"achieves":[202],"markedly":[203],"improved":[204],"performance":[205,220],"relative":[206],"standard":[208],"GCNs,":[209],"copes":[210],"state-of-the-art":[212],"adversarial":[213],"attacks,":[214],"leads":[216],"remarkable":[218],"SSL":[219],"over":[221],"protein-to-protein":[222],"interaction":[223],"networks.":[224]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}