{"id":"https://openalex.org/W4409494577","doi":"https://doi.org/10.1109/taffc.2025.3561439","title":"Two-Stream Dynamic Heterogeneous Graph Recurrent Neural Network for Multi-Label Multi-Modal Emotion Recognition","display_name":"Two-Stream Dynamic Heterogeneous Graph Recurrent Neural Network for Multi-Label Multi-Modal Emotion Recognition","publication_year":2025,"publication_date":"2025-04-16","ids":{"openalex":"https://openalex.org/W4409494577","doi":"https://doi.org/10.1109/taffc.2025.3561439"},"language":"en","primary_location":{"id":"doi:10.1109/taffc.2025.3561439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2025.3561439","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"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 Affective Computing","raw_type":"journal-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/A5100765674","display_name":"Jing Wang","orcid":"https://orcid.org/0000-0002-1017-2231"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Wang","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1017-2231","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002644777","display_name":"Zhiyang Feng","orcid":"https://orcid.org/0009-0009-8057-0217"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyang Feng","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008632096","display_name":"Xiaojun Ning","orcid":"https://orcid.org/0000-0002-3668-2950"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojun Ning","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3668-2950","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102790320","display_name":"Youfang Lin","orcid":"https://orcid.org/0000-0002-1611-4323"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Youfang Lin","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1611-4323","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077852542","display_name":"Badong Chen","orcid":"https://orcid.org/0000-0003-1710-3818"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Badong Chen","raw_affiliation_strings":["Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China","Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065497921","display_name":"Ziyu Jia","orcid":"https://orcid.org/0000-0002-8523-1419"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyu Jia","raw_affiliation_strings":["Beijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8523-1419","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":15.6769,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.99105983,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"16","issue":"3","first_page":"2396","last_page":"2409"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9106000065803528,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9106000065803528,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.906499981880188,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9042999744415283,"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/modal","display_name":"Modal","score":0.7187008261680603},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.7078202366828918},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5655021667480469},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46660277247428894},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4411916732788086},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4398888051509857},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35826557874679565},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2415594458580017}],"concepts":[{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.7187008261680603},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.7078202366828918},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5655021667480469},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46660277247428894},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4411916732788086},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4398888051509857},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35826557874679565},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2415594458580017},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taffc.2025.3561439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2025.3561439","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"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 Affective Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G4468112264","display_name":null,"funder_award_id":"62306317","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4739588433","display_name":null,"funder_award_id":"2023JBMC056","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1598933570","https://openalex.org/W1947251450","https://openalex.org/W2002055708","https://openalex.org/W2004104731","https://openalex.org/W2029334490","https://openalex.org/W2092939357","https://openalex.org/W2114872534","https://openalex.org/W2119417805","https://openalex.org/W2122098299","https://openalex.org/W2134031328","https://openalex.org/W2139564752","https://openalex.org/W2525648609","https://openalex.org/W2583542555","https://openalex.org/W2599124244","https://openalex.org/W2613375858","https://openalex.org/W2766736793","https://openalex.org/W2778812440","https://openalex.org/W2786768213","https://openalex.org/W2790404832","https://openalex.org/W2896297654","https://openalex.org/W2896745700","https://openalex.org/W2898664946","https://openalex.org/W2901337091","https://openalex.org/W2911286998","https://openalex.org/W2950978907","https://openalex.org/W2962905870","https://openalex.org/W2965857891","https://openalex.org/W2980064575","https://openalex.org/W2980159206","https://openalex.org/W2981372722","https://openalex.org/W2997258743","https://openalex.org/W2998313947","https://openalex.org/W3011240245","https://openalex.org/W3012664577","https://openalex.org/W3035652936","https://openalex.org/W3080551539","https://openalex.org/W3082734020","https://openalex.org/W3088256290","https://openalex.org/W3092835783","https://openalex.org/W3093125198","https://openalex.org/W3093434718","https://openalex.org/W3096230989","https://openalex.org/W3121280022","https://openalex.org/W3140416091","https://openalex.org/W3142509220","https://openalex.org/W3155886566","https://openalex.org/W3176028309","https://openalex.org/W3206241967","https://openalex.org/W4281710448"],"related_works":["https://openalex.org/W2379392295","https://openalex.org/W3160965418","https://openalex.org/W613940353","https://openalex.org/W2320915480","https://openalex.org/W2362990116","https://openalex.org/W2381300099","https://openalex.org/W2391251536","https://openalex.org/W2714992399","https://openalex.org/W3105646692","https://openalex.org/W4387914125"],"abstract_inverted_index":{"The":[0],"study":[1],"of":[2,10,62,97,115,139,161,171,176,186],"the":[3,36,46,49,60,63,73,137,140,157,169,174,184,198,209],"relationship":[4],"between":[5],"emotions":[6],"and":[7,19,48,107,125,147,159,189],"physiological":[8,53,163],"signals":[9,54],"subjects":[11],"under":[12],"multimedia":[13],"stimulation":[14],"is":[15,113],"an":[16],"emerging":[17],"field,":[18],"many":[20],"important":[21],"advances":[22],"are":[23,27,153],"made.":[24],"However,":[25],"there":[26],"still":[28],"some":[29],"challenges:":[30],"1)":[31],"How":[32,43,57,70],"to":[33,44,58,71,135,155,196,207],"effectively":[34],"utilize":[35],"complementarity":[37],"among":[38,51,75,211],"spatial-spectral-temporal":[39,141],"domain":[40,142],"information.":[41],"2)":[42],"employ":[45],"heterogeneity":[47,158],"correlation":[50,160],"multi-modal":[52,162],"simultaneously.":[55,143],"3)":[56],"improve":[59,197],"robustness":[61,172],"model":[64,72,156,208,224],"dealing":[65],"with":[66,168],"missing":[67,177],"channels.":[68],"4)":[69],"dependency":[74,210],"different":[76,212],"emotions.":[77],"In":[78,200],"this":[79],"paper,":[80],"we":[81,180,202],"propose":[82,130,203],"a":[83,98,101,104,108,116,131,191,204],"novel":[84],"two-stream":[85,133],"Dynamic":[86],"Heterogeneous":[87],"Graph":[88,144],"Recurrent":[89],"Neural":[90],"Network":[91],"called":[92],"DHGRNN.":[93],"Specifically,":[94],"DHGRNN":[95],"consists":[96],"spatial-temporal":[99],"stream,":[100,103],"spatial-spectral":[102],"fusion":[105],"layer,":[106],"multi-label":[109,205],"classifier.":[110],"Each":[111],"stream":[112],"composed":[114],"graph":[117,121,149,193],"transformer":[118,145],"network,":[119,124],"evolved":[120,148],"convolutional":[122,150],"neural":[123,151,194],"gated":[126],"recurrent":[127],"units.":[128],"We":[129],"graph-based":[132],"structure":[134],"fuse":[136],"information":[138],"network":[146,152,195],"used":[154],"signals,":[164],"respectively.":[165],"To":[166],"deal":[167],"problem":[170,185],"in":[173],"face":[175],"channel":[178],"data,":[179],"transform":[181],"it":[182],"into":[183],"dynamic":[187,192],"graphs":[188],"use":[190],"robustness.":[199],"addition,":[201],"classifier":[206],"emotion":[213],"dimensions.":[214],"Experiments":[215],"on":[216],"three":[217],"public":[218],"datasets":[219],"demonstrate":[220],"that":[221],"our":[222],"proposed":[223],"outperforms":[225],"existing":[226],"state-of-the-art":[227],"methods.":[228]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":6}],"updated_date":"2026-06-23T13:55:30.953635","created_date":"2025-10-10T00:00:00"}