{"id":"https://openalex.org/W4385902651","doi":"https://doi.org/10.1145/3581783.3611971","title":"Unsupervised Multiplex Graph learning with Complementary and Consistent Information","display_name":"Unsupervised Multiplex Graph learning with Complementary and Consistent Information","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4385902651","doi":"https://doi.org/10.1145/3581783.3611971"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3611971","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3581783.3611971","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3611971","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3611971","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100612023","display_name":"Liang Peng","orcid":"https://orcid.org/0000-0002-9831-2787"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Peng","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-9831-2787","affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327837","display_name":"Wang Xin","orcid":"https://orcid.org/0000-0001-8090-1834"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-8090-1834","affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037340898","display_name":"Xiaofeng Zhu","orcid":"https://orcid.org/0000-0001-6840-0578"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofeng Zhu","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-6840-0578","affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":1.5205,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.85149309,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"454","last_page":"462"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9993000030517578,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9993000030517578,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9585999846458435,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.958299994468689,"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":0.7942444086074829},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6166274547576904},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4824248254299164},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4577109217643738},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4567076861858368},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.45632174611091614},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4345826804637909},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.43056607246398926},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41813182830810547},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.392209529876709}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7942444086074829},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6166274547576904},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4824248254299164},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4577109217643738},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4567076861858368},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.45632174611091614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4345826804637909},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.43056607246398926},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41813182830810547},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.392209529876709},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3581783.3611971","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3581783.3611971","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3611971","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2308.01606","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2308.01606","pdf_url":"https://arxiv.org/pdf/2308.01606","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3581783.3611971","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3581783.3611971","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3611971","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1916619572","display_name":null,"funder_award_id":"2022 YFA1004100","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G2633038451","display_name":null,"funder_award_id":"ZYGX2022YGRH014","funder_id":"https://openalex.org/F4320323292","funder_display_name":"University of Electronic Science and Technology of China"},{"id":"https://openalex.org/G6683934479","display_name":null,"funder_award_id":"ZYGX2022YGRH009","funder_id":"https://openalex.org/F4320323292","funder_display_name":"University of Electronic Science and Technology of China"}],"funders":[{"id":"https://openalex.org/F4320323292","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385902651.pdf","grobid_xml":"https://content.openalex.org/works/W4385902651.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W2100235303","https://openalex.org/W2154851992","https://openalex.org/W2187089797","https://openalex.org/W2463786694","https://openalex.org/W2590019597","https://openalex.org/W2788379054","https://openalex.org/W2795735740","https://openalex.org/W2808361044","https://openalex.org/W2911286998","https://openalex.org/W2914547623","https://openalex.org/W2945266622","https://openalex.org/W2964012239","https://openalex.org/W2964015378","https://openalex.org/W2997686727","https://openalex.org/W3095746859","https://openalex.org/W3104097132","https://openalex.org/W3129850062","https://openalex.org/W3130828726","https://openalex.org/W3134210100","https://openalex.org/W3154503084","https://openalex.org/W3172710079","https://openalex.org/W3189601601","https://openalex.org/W3199950635","https://openalex.org/W3205065023","https://openalex.org/W3213009930","https://openalex.org/W4221160818","https://openalex.org/W4224324119","https://openalex.org/W4226087736","https://openalex.org/W4226183968","https://openalex.org/W4226216229","https://openalex.org/W4226299420","https://openalex.org/W4280590775","https://openalex.org/W4287179963","https://openalex.org/W4292423649","https://openalex.org/W4293254877","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W4297808394","https://openalex.org/W4303685257","https://openalex.org/W4304688807","https://openalex.org/W4306316983","https://openalex.org/W4322614756","https://openalex.org/W4367302253","https://openalex.org/W4385245566","https://openalex.org/W6600238479"],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W2142795561","https://openalex.org/W4205302943","https://openalex.org/W2561132942","https://openalex.org/W3155418658","https://openalex.org/W4243199227","https://openalex.org/W2379948177","https://openalex.org/W4285218279"],"abstract_inverted_index":{"Unsupervised":[0],"multiplex":[1],"graph":[2,79,94],"learning":[3,86],"(UMGL)":[4],"has":[5],"been":[6],"shown":[7],"to":[8,61,83,98,111],"achieve":[9],"significant":[10],"effectiveness":[11,125],"for":[12],"different":[13],"downstream":[14],"tasks":[15],"by":[16],"exploring":[17],"both":[18,63],"complementary":[19,64],"information":[20,23],"and":[21,41,57,65,103,126,132],"consistent":[22,66],"among":[24,96],"multiple":[25,74,108],"graphs.":[26],"However,":[27],"previous":[28],"methods":[29,131],"usually":[30],"overlook":[31],"the":[32,38,42,47,92,100,105,113,129],"issues":[33],"in":[34,50],"practical":[35],"applications,":[36],"i.e.,":[37,90],"out-of-sample":[39,101],"issue":[40],"noise":[43,114],"issue.":[44,115],"To":[45,68],"address":[46],"above":[48],"issues,":[49],"this":[51],"paper,":[52],"we":[53],"propose":[54],"an":[55],"effective":[56],"efficient":[58],"UMGL":[59],"method":[60,72,122],"explore":[62],"information.":[67],"do":[69],"this,":[70],"our":[71,120],"employs":[73],"MLP":[75],"encoders":[76],"rather":[77],"than":[78],"convolutional":[80],"network":[81],"(GCN)":[82],"conduct":[84],"representation":[85],"with":[87],"two":[88,136],"constraints,":[89],"preserving":[91],"local":[93],"structure":[95],"nodes":[97],"handle":[99,112],"issue,":[102],"maximizing":[104],"correlation":[106],"of":[107],"node":[109],"representations":[110],"Comprehensive":[116],"experiments":[117],"demonstrate":[118],"that":[119],"proposed":[121],"achieves":[123],"superior":[124],"efficiency":[127],"over":[128],"comparison":[130],"effectively":[133],"tackles":[134],"those":[135],"issues.":[137],"Code":[138],"is":[139],"available":[140],"at":[141],"https://github.com/LarryUESTC/CoCoMG.":[142]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
