{"id":"https://openalex.org/W3209785184","doi":"https://doi.org/10.1145/3459637.3482391","title":"Semi-supervised Multi-label Learning for Graph-structured Data","display_name":"Semi-supervised Multi-label Learning for Graph-structured Data","publication_year":2021,"publication_date":"2021-10-26","ids":{"openalex":"https://openalex.org/W3209785184","doi":"https://doi.org/10.1145/3459637.3482391","mag":"3209785184"},"language":"en","primary_location":{"id":"doi:10.1145/3459637.3482391","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research-information.bris.ac.uk/en/publications/5f9ce14c-8a7e-44f0-9a55-b5fff2a8e3d5","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036555049","display_name":"Zixing Song","orcid":"https://orcid.org/0000-0002-8871-3990"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zixing Song","raw_affiliation_strings":["The Chinese University of Hong Kong, Sha Tin, Hong Kong"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Sha Tin, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027386336","display_name":"Ziqiao Meng","orcid":"https://orcid.org/0000-0002-0231-5992"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziqiao Meng","raw_affiliation_strings":["The Chinese University of Hong Kong, Sha Tin, Hong Kong"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Sha Tin, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100386948","display_name":"Yifei Zhang","orcid":"https://orcid.org/0000-0003-4185-8663"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifei Zhang","raw_affiliation_strings":["The Chinese University of Hong Kong, Sha Tin, Hong Kong"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Sha Tin, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042251906","display_name":"Irwin King","orcid":"https://orcid.org/0000-0001-8106-6447"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Irwin King","raw_affiliation_strings":["The Chinese University of Hong Kong, Sha Tin, Hong Kong"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Sha Tin, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5036555049"],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":null,"apc_paid":null,"fwci":2.6598,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.91709903,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1723","last_page":"1733"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9995999932289124,"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":0.9995999932289124,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9987000226974487,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9887999892234802,"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":0.7215253710746765},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6346419453620911},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5262410044670105},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5158540606498718},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5124821662902832},{"id":"https://openalex.org/keywords/multi-label-classification","display_name":"Multi-label classification","score":0.4587019681930542},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.4551607668399811},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43801623582839966},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.430253267288208},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.42032235860824585},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3306746482849121}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7215253710746765},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6346419453620911},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5262410044670105},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5158540606498718},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5124821662902832},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.4587019681930542},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.4551607668399811},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43801623582839966},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.430253267288208},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.42032235860824585},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3306746482849121}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3459637.3482391","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:research-information.bris.ac.uk:openaire/5f9ce14c-8a7e-44f0-9a55-b5fff2a8e3d5","is_oa":true,"landing_page_url":"https://research-information.bris.ac.uk/en/publications/5f9ce14c-8a7e-44f0-9a55-b5fff2a8e3d5","pdf_url":null,"source":{"id":"https://openalex.org/S7407055359","display_name":"Explore Bristol Research","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":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Song, Z, Meng, Z, Zhang, Y & King, I 2021, Semi-supervised Multi-label Learning for Graph-structured Data. in The 30th ACM International Conference on Information & Knowledge Management. Association for Computing Machinery, pp. 1723-1733. https://doi.org/10.1145/3459637.3482391","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:research-information.bris.ac.uk:openaire_cris_publications/5f9ce14c-8a7e-44f0-9a55-b5fff2a8e3d5","is_oa":true,"landing_page_url":"https://hdl.handle.net/1983/5f9ce14c-8a7e-44f0-9a55-b5fff2a8e3d5","pdf_url":null,"source":{"id":"https://openalex.org/S7407055359","display_name":"Explore Bristol Research","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":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Song, Z, Meng, Z, Zhang, Y & King, I 2021, Semi-supervised Multi-label Learning for Graph-structured Data. in The 30th ACM International Conference on Information & Knowledge Management. Association for Computing Machinery, pp. 1723-1733. https://doi.org/10.1145/3459637.3482391","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"pmh:oai:research-information.bris.ac.uk:openaire/5f9ce14c-8a7e-44f0-9a55-b5fff2a8e3d5","is_oa":true,"landing_page_url":"https://research-information.bris.ac.uk/en/publications/5f9ce14c-8a7e-44f0-9a55-b5fff2a8e3d5","pdf_url":null,"source":{"id":"https://openalex.org/S7407055359","display_name":"Explore Bristol Research","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":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Song, Z, Meng, Z, Zhang, Y & King, I 2021, Semi-supervised Multi-label Learning for Graph-structured Data. in The 30th ACM International Conference on Information & Knowledge Management. Association for Computing Machinery, pp. 1723-1733. https://doi.org/10.1145/3459637.3482391","raw_type":"contributionToPeriodical"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1766074018","display_name":null,"funder_award_id":"No. 2018AAA0100204","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W2021968313","https://openalex.org/W2046253692","https://openalex.org/W2052684427","https://openalex.org/W2105543219","https://openalex.org/W2114315281","https://openalex.org/W2141923507","https://openalex.org/W2154851992","https://openalex.org/W2156935079","https://openalex.org/W2163776610","https://openalex.org/W2166851633","https://openalex.org/W2187089797","https://openalex.org/W2605997098","https://openalex.org/W2767849480","https://openalex.org/W2788462285","https://openalex.org/W2897806460","https://openalex.org/W2945031581","https://openalex.org/W2962756421","https://openalex.org/W2962975498","https://openalex.org/W2963858333","https://openalex.org/W2971325383","https://openalex.org/W2983966203","https://openalex.org/W2984838055","https://openalex.org/W2990477133","https://openalex.org/W2997546679","https://openalex.org/W3003906221","https://openalex.org/W3026423472","https://openalex.org/W3033623365","https://openalex.org/W3039423007","https://openalex.org/W3080933176","https://openalex.org/W3094411093","https://openalex.org/W3101643310","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W3114079967","https://openalex.org/W3120491054","https://openalex.org/W3127180058","https://openalex.org/W3157127187","https://openalex.org/W3177564060","https://openalex.org/W3187395669","https://openalex.org/W3187966659"],"related_works":["https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2912814903","https://openalex.org/W2123605750","https://openalex.org/W3045290893","https://openalex.org/W2088740331","https://openalex.org/W3038102983","https://openalex.org/W2950907416","https://openalex.org/W1559483280","https://openalex.org/W4394929514"],"abstract_inverted_index":{"The":[0,126],"semi-supervised":[1,37,95],"multi-label":[2,38,80,96,124],"classification":[3],"problem":[4,179],"primarily":[5],"deals":[6],"with":[7,13,21,69,132],"Euclidean":[8],"data,":[9],"such":[10],"as":[11,143,163,174],"text":[12],"a":[14,22,164,175],"1D":[15],"grid":[16,24],"of":[17,25,110,153],"tokens":[18],"and":[19,33,49,72,101,122,157],"images":[20],"2D":[23],"pixels.":[26],"However,":[27],"the":[28,55,79,83,94,108,133,138,144,151,154,169,182,201],"non-Euclidean":[29],"graph-structured":[30],"data":[31],"naturally":[32],"constantly":[34],"appears":[35],"in":[36,78],"learning":[39,92,97],"tasks":[40],"from":[41,192],"various":[42,193],"domains":[43,194],"like":[44,61],"social":[45],"networks,":[46,48],"citation":[47],"protein-protein":[50],"interaction":[51],"(PPI)":[52],"networks.":[53],"Moreover,":[54,150],"existing":[56],"popular":[57],"node":[58,134,158],"embedding":[59,112,128,135,156,159],"methods,":[60],"Graph":[62],"Neural":[63],"Networks":[64],"(GNN),":[65],"focus":[66],"on":[67,137,189],"graphs":[68],"simplex":[70],"labels":[71],"tend":[73],"to":[74,117,141,167,180],"neglect":[75],"label":[76,111,127,155,170],"correlations":[77],"setting,":[81],"so":[82],"easy":[84],"adaption":[85],"proves":[86],"empirically":[87],"ineffective.":[88],"Therefore,":[89],"graph":[90],"representation":[91],"for":[93,147,204],"task":[98],"is":[99,129],"crucial":[100],"challenging.":[102],"In":[103],"this":[104],"work,":[105],"we":[106],"incorporate":[107],"idea":[109],"into":[113],"our":[114,197],"proposed":[115],"model":[116,198],"capture":[118],"both":[119],"network":[120],"topology":[121],"higher-order":[123],"correlations.":[125],"generated":[130],"along":[131],"based":[136],"topological":[139],"structure":[140],"serve":[142],"prototype":[145],"center":[146],"each":[148],"class.":[149],"similarity":[152],"can":[160],"be":[161],"used":[162],"confidence":[165],"vector":[166],"guide":[168],"smoothing":[171],"process,":[172],"formulating":[173],"margin":[176],"ranking":[177],"optimization":[178],"learn":[181],"second-order":[183],"relations":[184],"between":[185],"labels.":[186],"Extensive":[187],"experiments":[188],"real-world":[190],"datasets":[191],"demonstrate":[195],"that":[196],"significantly":[199],"outperforms":[200],"state-of-the-art":[202],"models":[203],"node-level":[205],"tasks.":[206]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":8}],"updated_date":"2026-04-15T08:11:43.952461","created_date":"2025-10-10T00:00:00"}