{"id":"https://openalex.org/W4415538222","doi":"https://doi.org/10.1145/3746027.3755812","title":"CODE: Towards Partial Label Graph Learning via Coupled Dual Separation","display_name":"CODE: Towards Partial Label Graph Learning via Coupled Dual Separation","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415538222","doi":"https://doi.org/10.1145/3746027.3755812"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755812","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755812","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","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/A5049810016","display_name":"Yiyang Gu","orcid":"https://orcid.org/0000-0002-5915-4448"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiyang Gu","raw_affiliation_strings":["National Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5915-4448","affiliations":[{"raw_affiliation_string":"National Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075863200","display_name":"Taian Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Taian Guo","raw_affiliation_strings":["National Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-2401-4228","affiliations":[{"raw_affiliation_string":"National Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hang Zhou","orcid":"https://orcid.org/0009-0003-9535-6287"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hang Zhou","raw_affiliation_strings":["University of California, Davis, Davis, CA, USA"],"raw_orcid":"https://orcid.org/0009-0003-9535-6287","affiliations":[{"raw_affiliation_string":"University of California, Davis, Davis, CA, USA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090565304","display_name":"Zihao Chen","orcid":"https://orcid.org/0000-0002-1382-0409"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihao Chen","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1382-0409","affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049472306","display_name":"Zhiping Xiao","orcid":"https://orcid.org/0000-0002-8583-4789"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiping Xiao","raw_affiliation_strings":["University of Washington, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0002-8583-4789","affiliations":[{"raw_affiliation_string":"University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058440014","display_name":"Yifang Qin","orcid":"https://orcid.org/0000-0002-7520-8039"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifang Qin","raw_affiliation_strings":["National Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7520-8039","affiliations":[{"raw_affiliation_string":"National Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100426938","display_name":"Xiao Luo","orcid":"https://orcid.org/0000-0002-7987-3714"},"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":"Xiao Luo","raw_affiliation_strings":["University of California, Los Angeles, Los Angeles, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-7987-3714","affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018666299","display_name":"Wei Ju","orcid":"https://orcid.org/0000-0001-9657-951X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Ju","raw_affiliation_strings":["National Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9657-951X","affiliations":[{"raw_affiliation_string":"National Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026207516","display_name":"Yifan Wang","orcid":"https://orcid.org/0000-0001-7764-8698"},"institutions":[{"id":"https://openalex.org/I146563203","display_name":"University of International Business and Economics","ror":"https://ror.org/05khqpb71","country_code":"CN","type":"education","lineage":["https://openalex.org/I146563203"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifan Wang","raw_affiliation_strings":["University of International Business and Economics, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7764-8698","affiliations":[{"raw_affiliation_string":"University of International Business and Economics, Beijing, China","institution_ids":["https://openalex.org/I146563203"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100642537","display_name":"Ming Zhang","orcid":"https://orcid.org/0000-0002-9809-3430"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Zhang","raw_affiliation_strings":["National Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9809-3430","affiliations":[{"raw_affiliation_string":"National Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8949","last_page":"8958"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9979000091552734,"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":0.9979000091552734,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9684000015258789,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/graph","display_name":"Graph","score":0.564300000667572},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4729999899864197},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.47049999237060547},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.37689998745918274},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3508000075817108},{"id":"https://openalex.org/keywords/clique-width","display_name":"Clique-width","score":0.34929999709129333},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.32679998874664307}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6736000180244446},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.564300000667572},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4729999899864197},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.47049999237060547},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4571000039577484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40139999985694885},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.37689998745918274},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C5737132","wikidata":"https://www.wikidata.org/wiki/Q1101814","display_name":"Clique-width","level":5,"score":0.34929999709129333},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3197999894618988},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C100595998","wikidata":"https://www.wikidata.org/wiki/Q11731931","display_name":"Graph kernel","level":5,"score":0.2906999886035919},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28690001368522644},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C558772884","wikidata":"https://www.wikidata.org/wiki/Q1508564","display_name":"Graph rewriting","level":3,"score":0.2612999975681305},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3755812","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755812","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5244014665","display_name":null,"funder_award_id":"No. 62276002","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2007016642","https://openalex.org/W2128332443","https://openalex.org/W2147286743","https://openalex.org/W2250662230","https://openalex.org/W2765579990","https://openalex.org/W2905443329","https://openalex.org/W2925177113","https://openalex.org/W2997519153","https://openalex.org/W2997997679","https://openalex.org/W3034184697","https://openalex.org/W3211726607","https://openalex.org/W4205091644","https://openalex.org/W4213077304","https://openalex.org/W4304014903","https://openalex.org/W4304091743","https://openalex.org/W4310012576","https://openalex.org/W4327808327","https://openalex.org/W4386453575","https://openalex.org/W4386977577","https://openalex.org/W4387969747","https://openalex.org/W4399768823"],"related_works":[],"abstract_inverted_index":{"Graph":[0],"classification":[1,18],"is":[2,61,74],"a":[3,64,96,115,120],"fundamental":[4],"machine":[5],"learning":[6],"problem":[7],"with":[8],"extensive":[9,36],"applications":[10],"in":[11,32,57,81,171],"multimedia":[12],"and":[13,85,119,129,149,178],"biochemical":[14],"analysis.":[15],"Contemporary":[16],"graph":[17,23,55,60,83,106,121,126],"models":[19],"usually":[20],"require":[21],"precise":[22,37],"labels":[24,68],"for":[25,156,181],"supervision,":[26],"even":[27],"after":[28],"self-supervised":[29],"pre-training.":[30],"However,":[31],"practical":[33],"applications,":[34],"the":[35,86,157,160,166,192,195],"annotation":[38],"of":[39,66,72,88,159,168,194],"graphs":[40,144],"could":[41],"be":[42],"expensive":[43],"or":[44],"impractical.":[45],"To":[46,104,132],"exploit":[47],"data":[48],"efficiently,":[49],"this":[50],"work":[51],"studies":[52],"partial":[53,91],"label":[54,110],"learning,":[56],"which":[58,124,153],"each":[59],"linked":[62],"to":[63,140,164],"set":[65,148],"candidate":[67],"but":[69],"only":[70],"one":[71,138],"them":[73],"accurate.":[75],"Label":[76],"ambiguity":[77],"would":[78],"bring":[79],"difficulties":[80],"extracting":[82],"semantics":[84,107,127],"risk":[87,167],"overfitting":[89],"noisy":[90],"labels.":[92],"Here,":[93],"we":[94,136],"present":[95],"novel":[97],"approach":[98],"called":[99],"Coupled":[100],"Dual":[101],"Separation":[102],"(CODE).":[103],"improve":[105],"mining":[108],"under":[109],"ambiguity,":[111],"our":[112],"CODE":[113],"contains":[114],"message":[116],"passing":[117],"branch":[118,139],"kernel":[122],"branch,":[123],"explore":[125],"implicitly":[128],"explicitly,":[130],"respectively.":[131],"facilitate":[133],"information":[134],"exchange,":[135],"utilize":[137],"separate":[141],"partially":[142],"labeled":[143],"into":[145,176],"an":[146,150],"informative":[147],"uninformative":[151],"set,":[152],"provides":[154],"guidance":[155],"optimization":[158,183],"other":[161],"branch.":[162],"Furthermore,":[163],"mitigate":[165],"overfitting,":[169],"parameters":[170],"coupled":[172],"branches":[173],"are":[174],"partitioned":[175],"critical":[177],"non-critical":[179],"ones":[180],"separated":[182],"procedures.":[184],"Extensive":[185],"experiments":[186],"on":[187],"several":[188],"benchmark":[189],"datasets":[190],"validate":[191],"effectiveness":[193],"proposed":[196],"CODE.":[197]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-25T00:00:00"}
