{"id":"https://openalex.org/W4402351455","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651179","title":"Using Joint Training for Hybrid Automated Augmentations in Graph Contrastive Learning","display_name":"Using Joint Training for Hybrid Automated Augmentations in Graph Contrastive Learning","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351455","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651179"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651179","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651179","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/A5100767069","display_name":"Yifu Chen","orcid":"https://orcid.org/0000-0003-4674-5705"},"institutions":[{"id":"https://openalex.org/I1300757298","display_name":"Heilongjiang University of Science and Technology","ror":"https://ror.org/030xwyx96","country_code":"CN","type":"education","lineage":["https://openalex.org/I1300757298"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifu Chen","raw_affiliation_strings":["Heilongjiang University,Department of Computer Science and Technology,Harbin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Heilongjiang University,Department of Computer Science and Technology,Harbin,China","institution_ids":["https://openalex.org/I1300757298"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016869122","display_name":"Qianqian Ren","orcid":"https://orcid.org/0000-0003-0235-2096"},"institutions":[{"id":"https://openalex.org/I1300757298","display_name":"Heilongjiang University of Science and Technology","ror":"https://ror.org/030xwyx96","country_code":"CN","type":"education","lineage":["https://openalex.org/I1300757298"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qianqian Ren","raw_affiliation_strings":["Heilongjiang University,Department of Computer Science and Technology,Harbin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Heilongjiang University,Department of Computer Science and Technology,Harbin,China","institution_ids":["https://openalex.org/I1300757298"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1300757298"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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":0.9944000244140625,"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/T10028","display_name":"Topic Modeling","score":0.9776999950408936,"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":0.7703826427459717},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5894556045532227},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.510810136795044},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4866398274898529},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.47913071513175964},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4650389552116394},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4174901843070984},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.15817993879318237},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09889158606529236}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7703826427459717},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5894556045532227},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.510810136795044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4866398274898529},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.47913071513175964},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4650389552116394},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4174901843070984},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.15817993879318237},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09889158606529236},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651179","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651179","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2122925692","https://openalex.org/W2547875792","https://openalex.org/W2887997457","https://openalex.org/W2907492528","https://openalex.org/W2908442265","https://openalex.org/W2962711740","https://openalex.org/W2976016473","https://openalex.org/W3005552578","https://openalex.org/W3012816161","https://openalex.org/W3026092005","https://openalex.org/W3033039844","https://openalex.org/W3035524453","https://openalex.org/W3035739162","https://openalex.org/W3036446966","https://openalex.org/W3042313988","https://openalex.org/W3086452730","https://openalex.org/W3094193403","https://openalex.org/W3095602948","https://openalex.org/W3095746859","https://openalex.org/W3099152386","https://openalex.org/W3108655343","https://openalex.org/W3126928293","https://openalex.org/W3154503084","https://openalex.org/W3167620929","https://openalex.org/W3168406300","https://openalex.org/W3190214286","https://openalex.org/W3199755688","https://openalex.org/W4283796586","https://openalex.org/W6677656871","https://openalex.org/W6755573351","https://openalex.org/W6768312477","https://openalex.org/W6772452955","https://openalex.org/W6777179611","https://openalex.org/W6784694379","https://openalex.org/W6784958482","https://openalex.org/W6795898371","https://openalex.org/W6796597629"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W100620283","https://openalex.org/W2495260952","https://openalex.org/W4394050964","https://openalex.org/W2551249631"],"abstract_inverted_index":{"Graph":[0],"contrastive":[1,80,140],"learning":[2],"under":[3],"a":[4,126,170],"single":[5],"augmentation":[6,62,72,95,113,161],"often":[7,53],"fails":[8],"to":[9,19,26,41,105,173],"fully":[10],"capture":[11],"the":[12,15,39,48,55,65,71,76,79,87,90,107,111,160,176,181],"properties":[13],"of":[14,50,57,70,78,89,109,151,183],"graph.":[16],"In":[17],"order":[18],"address":[20,121],"this":[21],"limitation,":[22],"it":[23,145],"is":[24,83],"beneficial":[25],"utilize":[27],"hybrid":[28,135],"augmentations,":[29,35],"such":[30],"as":[31,36],"edge":[32],"and":[33,82,118],"node-level":[34],"they":[37,99],"enable":[38],"model":[40],"learn":[42],"more":[43],"robust":[44],"semantic":[45],"commonalities.":[46],"However":[47],"combination":[49,182],"multiple":[51],"augmentations":[52,137,152],"faces":[54],"challenge":[56,108],"complex":[58],"parameter":[59],"tuning,":[60],"with":[61,180],"rate":[63,73,162],"being":[64],"most":[66],"crucial.":[67],"The":[68],"magnitude":[69],"directly":[74],"affects":[75],"semantics":[77],"views":[81],"therefore":[84],"crucial":[85],"for":[86,134],"performance":[88],"model.":[91],"Although":[92],"automated":[93,136,149,185],"data":[94],"methods":[96],"have":[97],"emerged,":[98],"either":[100],"require":[101],"labels":[102,156],"or":[103],"struggle":[104],"overcome":[106],"determining":[110],"appropriate":[112],"rate,":[114],"necessitating":[115],"extensive":[116],"evaluation":[117],"trial-and-error.":[119],"To":[120],"these":[122],"challenges,":[123],"we":[124,168],"propose":[125],"new":[127],"framework":[128],"(JTAGCL)":[129],"that":[130],"employs":[131],"joint":[132],"training":[133],"in":[138,163],"graph":[139],"learning.":[141],"By":[142],"doing":[143],"so,":[144],"not":[146],"only":[147],"achieves":[148],"optimization":[150],"without":[153],"relying":[154],"on":[155],"but":[157],"also":[158],"adapts":[159],"an":[164],"adaptive":[165],"manner.":[166],"Furthermore,":[167],"design":[169],"sampling":[171],"strategy":[172],"further":[174],"alleviate":[175],"computational":[177],"burden":[178],"associated":[179],"two":[184],"augmentations.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
