{"id":"https://openalex.org/W4403578285","doi":"https://doi.org/10.1145/3627673.3679887","title":"CNN to GNN: Unsupervised Multi-level Knowledge Learning","display_name":"CNN to GNN: Unsupervised Multi-level Knowledge Learning","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4403578285","doi":"https://doi.org/10.1145/3627673.3679887"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3679887","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3679887","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 Information and Knowledge Management","raw_type":"proceedings-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/A5028689634","display_name":"Ziheng Jiao","orcid":"https://orcid.org/0000-0002-8342-7311"},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ziheng Jiao","raw_affiliation_strings":["Institute of Artificial Intelligence (TeleAI), China Telecom, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence (TeleAI), China Telecom, Shanghai, China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100607664","display_name":"Hongyuan Zhang","orcid":"https://orcid.org/0000-0003-4274-7332"},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyuan Zhang","raw_affiliation_strings":["Institute of Artificial Intelligence (TeleAI), China Telecom, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence (TeleAI), China Telecom, Shanghai, China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100740143","display_name":"Xuelong Li","orcid":"https://orcid.org/0000-0002-0019-4197"},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuelong Li","raw_affiliation_strings":["Institute of Artificial Intelligence (TeleAI), China Telecom, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence (TeleAI), China Telecom, Shanghai, China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5028689634"],"corresponding_institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"],"apc_list":null,"apc_paid":null,"fwci":0.3637,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.67998214,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"4313","last_page":"4317"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9969000220298767,"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/T10320","display_name":"Neural Networks and Applications","score":0.9969000220298767,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9958000183105469,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9940000176429749,"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":0.7787298560142517},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.569495677947998},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.508716881275177},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3497656583786011}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7787298560142517},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.569495677947998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.508716881275177},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3497656583786011}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627673.3679887","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3679887","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 Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5400000214576721,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1979089718","https://openalex.org/W2076363162","https://openalex.org/W2106115875","https://openalex.org/W2125531986","https://openalex.org/W2169658215","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2767404761","https://openalex.org/W2963465695","https://openalex.org/W2977618190","https://openalex.org/W3197069066","https://openalex.org/W3207496032","https://openalex.org/W3212924718","https://openalex.org/W4206441147","https://openalex.org/W4390246037","https://openalex.org/W4391147885","https://openalex.org/W4391984607","https://openalex.org/W4395029107","https://openalex.org/W6837684022"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Although":[0],"graph":[1,13,43,101],"neural":[2,36],"networks":[3,37],"(GNNs)":[4],"can":[5,119],"extract":[6,98,125],"the":[7,12,28,34,42,46,69,75,99,105,115,121,143],"latent":[8],"relationship-level":[9,72],"knowledge":[10,30,63,73,123],"among":[11],"nodes":[14],"and":[15,71,103,124],"have":[16],"achieved":[17,138],"excellent":[18,139],"performance":[19,140],"in":[20,26,31],"unsupervised":[21,61],"scenarios,":[22],"it":[23,136],"is":[24,94],"weak":[25],"learning":[27],"instance-level":[29,70],"contrast":[32],"to":[33,85,96],"convolution":[35],"(CNNs).":[38],"Besides,":[39],"lacking":[40],"of":[41,48,112],"structure":[44],"limits":[45],"extension":[47],"GNNs":[49],"on":[50,74,107],"non-graph":[51,76,108],"datasets.":[52,109],"To":[53],"solve":[54],"these":[55],"problems,":[56],"we":[57],"propose":[58],"a":[59,81,86,90],"novel":[60],"multi-level":[62,122],"fusion":[64],"network.":[65],"It":[66],"successfully":[67],"unifies":[68],"data":[77],"by":[78],"distillation":[79,113],"from":[80],"pre-trained":[82],"CNN":[83],"teacher":[84],"GNN":[87,106,117],"student.":[88],"Meanwhile,":[89],"sparse":[91,100],"weighted":[92],"strategy":[93],"designed":[95],"adaptively":[97],"topology":[102],"extend":[104],"By":[110],"optimization":[111],"loss,":[114],"\"boosted''":[116],"student":[118],"learn":[120],"more":[126],"discriminative":[127],"deep":[128],"embeddings":[129],"for":[130],"clustering.":[131],"Finally,":[132],"extensive":[133],"experiments":[134],"show":[135],"has":[137],"compared":[141],"with":[142],"current":[144],"methods.":[145]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
