{"id":"https://openalex.org/W4412610592","doi":"https://doi.org/10.1109/hpcc64274.2024.00186","title":"Incremental Label Distribution Learning with Scalable Graph Convolutional Networks","display_name":"Incremental Label Distribution Learning with Scalable Graph Convolutional Networks","publication_year":2024,"publication_date":"2024-12-13","ids":{"openalex":"https://openalex.org/W4412610592","doi":"https://doi.org/10.1109/hpcc64274.2024.00186"},"language":"en","primary_location":{"id":"doi:10.1109/hpcc64274.2024.00186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpcc64274.2024.00186","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on High Performance Computing and Communications (HPCC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5075343454","display_name":"Ziqi Jia","orcid":"https://orcid.org/0009-0007-8130-7782"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]},{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziqi Jia","raw_affiliation_strings":["Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101586244","display_name":"Xiaoyang Qu","orcid":"https://orcid.org/0000-0001-8353-4064"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]},{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyang Qu","raw_affiliation_strings":["Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101721522","display_name":"Chenghao Liu","orcid":"https://orcid.org/0000-0002-6934-2354"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenghao Liu","raw_affiliation_strings":["Tsinghua University,Tsinghua Shenzhen International Graduate School,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Tsinghua Shenzhen International Graduate School,Shenzhen,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074472751","display_name":"Jianzong Wang","orcid":"https://orcid.org/0000-0002-9237-4231"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]},{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianzong Wang","raw_affiliation_strings":["Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35592497,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1394","last_page":"1400"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9897000193595886,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9897000193595886,"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":0.9050999879837036,"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":0.789385199546814},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6082218885421753},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.49962711334228516},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.36104241013526917},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35956814885139465},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08831080794334412}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.789385199546814},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6082218885421753},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.49962711334228516},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.36104241013526917},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35956814885139465},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08831080794334412}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hpcc64274.2024.00186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpcc64274.2024.00186","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on High Performance Computing and Communications (HPCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2066454034","https://openalex.org/W2075381877","https://openalex.org/W2330485005","https://openalex.org/W2473930607","https://openalex.org/W2510725918","https://openalex.org/W2553156677","https://openalex.org/W2791091755","https://openalex.org/W2807904173","https://openalex.org/W2895723011","https://openalex.org/W2954929116","https://openalex.org/W2963321416","https://openalex.org/W2964189064","https://openalex.org/W2976049311","https://openalex.org/W3030364939","https://openalex.org/W3096840866","https://openalex.org/W4214924370","https://openalex.org/W4312308876","https://openalex.org/W4320040766","https://openalex.org/W4372330959","https://openalex.org/W4392904751","https://openalex.org/W4393159493","https://openalex.org/W4400624296","https://openalex.org/W4400680576","https://openalex.org/W6694232893","https://openalex.org/W6732436211","https://openalex.org/W6753198114","https://openalex.org/W6760001035","https://openalex.org/W6779335803"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2389214306"],"abstract_inverted_index":{"Label":[0,133,151],"Distribution":[1,134,152],"Learning":[2,71,135,153],"(LDL)":[3],"is":[4,121],"an":[5],"effective":[6],"approach":[7],"for":[8,159,218],"handling":[9],"label":[10,27,53],"ambiguity,":[11],"as":[12,59,155,184],"it":[13],"can":[14],"analyze":[15,137],"all":[16,79],"labels":[17,40,75,80,90,104,179],"at":[18,81],"once":[19],"and":[20,91,144,147,180,210],"indicate":[21],"the":[22,37,52,60,88,95,99,106,111,175,189,199,204,212,219],"extent":[23],"to":[24,41,115,172,187,192],"which":[25],"each":[26],"describes":[28],"a":[29,65,123,156,167,185,215],"given":[30],"sample.":[31],"Most":[32],"existing":[33,68],"LDL":[34,107,201],"methods":[35,69],"consider":[36],"number":[38],"of":[39,62,118,177,207,214],"be":[42],"static.":[43],"However,":[44],"in":[45,163],"various":[46],"LDL-specific":[47],"contexts":[48],"(e.g.,":[49],"disease":[50],"diagnosis),":[51],"count":[54],"grows":[55],"over":[56],"time":[57,86,190],"(such":[58],"discovery":[61],"new":[63,74,103,178],"diseases),":[64],"factor":[66],"that":[67],"overlook.":[70],"samples":[72,143],"with":[73],"directly":[76],"means":[77,109],"learning":[78,102,176],"once,":[82],"thus":[83],"wasting":[84],"more":[85],"on":[87,198],"old":[89,96],"even":[92],"risking":[93],"overfitting":[94],"labels.":[97],"At":[98],"same":[100],"time,":[101],"by":[105],"model":[108],"reconstructing":[110],"inter-label":[112,145,182,194],"relationships.":[113,195],"How":[114],"make":[116],"use":[117],"constructed":[119],"relationships":[120,183],"also":[122],"crucial":[124],"challenge.":[125],"To":[126],"tackle":[127],"these":[128],"challenges,":[129],"we":[130,165],"introduce":[131],"Incremental":[132],"(ILDL),":[136],"its":[138],"key":[139],"issues":[140],"regarding":[141],"training":[142],"relationships,":[146],"propose":[148],"Scalable":[149],"Graph":[150],"(SGLDL)":[154],"practical":[157],"framework":[158],"implementing":[160],"ILDL.":[161],"Specifically,":[162],"SGLDL,":[164],"develop":[166],"New-label-aware":[168],"Gradient":[169],"Compensation":[170],"Loss":[171],"speed":[173],"up":[174],"represent":[181],"graph":[186],"reduce":[188],"required":[191],"reconstruct":[193],"Experimental":[196],"results":[197],"classical":[200],"dataset":[202],"show":[203],"clear":[205],"advantages":[206],"unique":[208],"algorithms":[209],"illustrate":[211],"importance":[213],"dedicated":[216],"design":[217],"ILDL":[220],"problem.":[221]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
