{"id":"https://openalex.org/W4406890294","doi":"https://doi.org/10.26599/bdma.2024.9020060","title":"SCoAMPS: Semi-Supervised Graph Contrastive Learning Based on Associative Memory Network and Pseudo-Label Similarity","display_name":"SCoAMPS: Semi-Supervised Graph Contrastive Learning Based on Associative Memory Network and Pseudo-Label Similarity","publication_year":2025,"publication_date":"2025-01-28","ids":{"openalex":"https://openalex.org/W4406890294","doi":"https://doi.org/10.26599/bdma.2024.9020060"},"language":"en","primary_location":{"id":"doi:10.26599/bdma.2024.9020060","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2024.9020060","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654","2097-406X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.26599/bdma.2024.9020060","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zaigang Gong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zaigang Gong","raw_affiliation_strings":["State Grid Yangzhou Power Supply Company,Yangzhou,China,225100"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Yangzhou Power Supply Company,Yangzhou,China,225100","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013834849","display_name":"Siyu Chen","orcid":"https://orcid.org/0000-0002-6582-0945"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siyu Chen","raw_affiliation_strings":["State Grid Yangzhou Power Supply Company,Yangzhou,China,225100"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Yangzhou Power Supply Company,Yangzhou,China,225100","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046416400","display_name":"Qiangsheng Dai","orcid":"https://orcid.org/0000-0002-1162-3030"},"institutions":[{"id":"https://openalex.org/I4210126065","display_name":"Shanghai Electric (China)","ror":"https://ror.org/0314qy595","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiangsheng Dai","raw_affiliation_strings":["State Grid Jiangsu Electric Power Co. Ltd.,Nanjing,China,210000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Jiangsu Electric Power Co. Ltd.,Nanjing,China,210000","institution_ids":["https://openalex.org/I4210126065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100398504","display_name":"Ying Feng","orcid":"https://orcid.org/0000-0003-1045-4172"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ying Feng","raw_affiliation_strings":["State Grid Yangzhou Power Supply Company,Yangzhou,China,225100"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Yangzhou Power Supply Company,Yangzhou,China,225100","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100388749","display_name":"Jiawei Wang","orcid":"https://orcid.org/0000-0001-5868-9531"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Wang","raw_affiliation_strings":["School of Cyber Science and Engineering, Southeast University,Nanjing,China,211189"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Cyber Science and Engineering, Southeast University,Nanjing,China,211189","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100436590","display_name":"Jinghui Zhang","orcid":"https://orcid.org/0000-0002-9067-7896"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinghui Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, Southeast University,Nanjing,China,211189"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Southeast University,Nanjing,China,211189","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.584,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.9501292,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"8","issue":"2","first_page":"273","last_page":"291"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.992900013923645,"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":0.992900013923645,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9552000164985657,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9455999732017517,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/associative-property","display_name":"Associative property","score":0.6086617708206177},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6053434610366821},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5660396218299866},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5535191893577576},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5390642881393433},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5337173938751221},{"id":"https://openalex.org/keywords/content-addressable-memory","display_name":"Content-addressable memory","score":0.4515286684036255},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3796325922012329},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2697465419769287},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.21938765048980713},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21838974952697754}],"concepts":[{"id":"https://openalex.org/C159423971","wikidata":"https://www.wikidata.org/wiki/Q177251","display_name":"Associative property","level":2,"score":0.6086617708206177},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6053434610366821},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5660396218299866},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5535191893577576},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5390642881393433},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5337173938751221},{"id":"https://openalex.org/C53442348","wikidata":"https://www.wikidata.org/wiki/Q745101","display_name":"Content-addressable memory","level":3,"score":0.4515286684036255},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3796325922012329},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2697465419769287},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.21938765048980713},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21838974952697754},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.26599/bdma.2024.9020060","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2024.9020060","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654","2097-406X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a8538c637154456fa77378fe02015a41","is_oa":true,"landing_page_url":"https://doaj.org/article/a8538c637154456fa77378fe02015a41","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data Mining and Analytics, Vol 8, Iss 2, Pp 273-291 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.26599/bdma.2024.9020060","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2024.9020060","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654","2097-406X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1531674615","https://openalex.org/W2007016642","https://openalex.org/W2106474670","https://openalex.org/W2147286743","https://openalex.org/W2230728100","https://openalex.org/W2726670313","https://openalex.org/W2788919350","https://openalex.org/W2908404712","https://openalex.org/W2913015533","https://openalex.org/W2962876364","https://openalex.org/W2964159205","https://openalex.org/W3030071125","https://openalex.org/W3035524453","https://openalex.org/W3041085747","https://openalex.org/W3095746859","https://openalex.org/W3173210285","https://openalex.org/W3176643558","https://openalex.org/W3177028972","https://openalex.org/W3199755688","https://openalex.org/W3200650376","https://openalex.org/W3215452784","https://openalex.org/W4225338086","https://openalex.org/W4281696987","https://openalex.org/W4282943426","https://openalex.org/W4289533979","https://openalex.org/W4391853848","https://openalex.org/W4392208003","https://openalex.org/W4393159841","https://openalex.org/W4393160201","https://openalex.org/W6681029592","https://openalex.org/W6681588610","https://openalex.org/W6717772578","https://openalex.org/W6726873649","https://openalex.org/W6733814495","https://openalex.org/W6736685754","https://openalex.org/W6755573351","https://openalex.org/W6766156693","https://openalex.org/W6771787070","https://openalex.org/W6774314701","https://openalex.org/W6776488958","https://openalex.org/W6776700526","https://openalex.org/W6780444311","https://openalex.org/W6784694379","https://openalex.org/W6796768152","https://openalex.org/W6845837341","https://openalex.org/W7065437122"],"related_works":["https://openalex.org/W1492794944","https://openalex.org/W169603398","https://openalex.org/W2953297905","https://openalex.org/W2049033869","https://openalex.org/W279701215","https://openalex.org/W2107697999","https://openalex.org/W1984090010","https://openalex.org/W1902246517","https://openalex.org/W2140603008","https://openalex.org/W3145837419"],"abstract_inverted_index":{"Graph":[0,17],"data":[1,47,103],"have":[2],"extensive":[3],"applications":[4],"in":[5,31,63,98],"various":[6],"domains,":[7],"including":[8],"social":[9],"networks,":[10,13],"biological":[11],"reaction":[12],"and":[14,92,104,125,145,151],"molecular":[15],"structures.":[16],"classification":[18,84],"aims":[19],"to":[20,156],"predict":[21],"the":[22,49,55],"properties":[23],"of":[24,45,57,73],"entire":[25],"graphs,":[26],"playing":[27],"a":[28,42,70,115],"crucial":[29],"role":[30],"many":[32],"downstream":[33],"applications.":[34],"However,":[35],"existing":[36],"graph":[37,83,102,117],"neural":[38],"network":[39,124,155],"methods":[40],"require":[41],"large":[43],"amount":[44],"labeled":[46,64],"during":[48],"training":[50,75],"process.":[51],"In":[52],"real-world":[53],"scenarios,":[54],"acquisition":[56],"labels":[58],"is":[59],"extremely":[60],"costly,":[61],"resulting":[62],"samples":[65,147],"typically":[66],"accounting":[67],"for":[68],"only":[69],"small":[71],"portion":[72],"all":[74],"data,":[76],"which":[77],"limits":[78],"model":[79],"performance.":[80],"Current":[81],"semi-supervised":[82],"methods,":[85],"such":[86],"as":[87],"those":[88],"based":[89,120],"on":[90,121,170],"pseudo-labels":[91],"knowledge":[93],"distillation,":[94],"still":[95],"face":[96],"limitations":[97],"effectively":[99],"utilizing":[100],"unlabeled":[101],"mitigating":[105],"pseudo-label":[106,149,158],"bias":[107,159],"issues.":[108],"To":[109],"address":[110],"these":[111],"challenges,":[112],"we":[113],"propose":[114],"Semi-supervised":[116],"Contrastive":[118],"learning":[119,135],"Associative":[122],"Memory":[123],"Pseudo-label":[126],"Similarity":[127],"(SCoAMPS).":[128],"SCoAMPS":[129,165],"integrates":[130],"pseudo-labeling":[131],"techniques":[132],"with":[133],"contrastive":[134,138],"by":[136],"generating":[137],"views":[139],"through":[140],"multiple":[141,171],"encoders,":[142],"selecting":[143],"positive":[144],"negative":[146],"using":[148],"similarity,":[150],"defining":[152],"associative":[153],"memory":[154],"alleviate":[157],"problems.":[160],"Experimental":[161],"results":[162],"demonstrate":[163],"that":[164],"achieves":[166],"significant":[167],"performance":[168],"improvements":[169],"public":[172],"datasets.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}
