{"id":"https://openalex.org/W4415708106","doi":"https://doi.org/10.1109/icme59968.2025.11210233","title":"GA-Clip: Semantic-Aware Graph Augmentation for Contrastive Learning","display_name":"GA-Clip: Semantic-Aware Graph Augmentation for Contrastive Learning","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415708106","doi":"https://doi.org/10.1109/icme59968.2025.11210233"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11210233","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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/A5101307582","display_name":"Shuaiqi Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuaiqi Lu","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Automation,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Automation,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049706279","display_name":"Yi Guo","orcid":"https://orcid.org/0000-0002-7142-2871"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Guo","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Automation,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Automation,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001581332","display_name":"Zhenlin An","orcid":"https://orcid.org/0000-0003-4120-773X"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhenlin An","raw_affiliation_strings":["University of Pittsburgh,Department of Computer Science,Pittsburgh,PA,USA"],"affiliations":[{"raw_affiliation_string":"University of Pittsburgh,Department of Computer Science,Pittsburgh,PA,USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100434303","display_name":"Yan Zhu","orcid":"https://orcid.org/0000-0001-5308-8630"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Zhu","raw_affiliation_strings":["College of Science University of Shanghai for Science and Technology,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"College of Science University of Shanghai for Science and Technology,Shanghai,China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022693200","display_name":"Ning Huang","orcid":"https://orcid.org/0000-0003-2044-4023"},"institutions":[{"id":"https://openalex.org/I4210103346","display_name":"Nanfang Hospital","ror":"https://ror.org/01eq10738","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210103346"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Huang","raw_affiliation_strings":["Nanfang College Guangzhou,Department of Public Administration,Guangdong,China"],"affiliations":[{"raw_affiliation_string":"Nanfang College Guangzhou,Department of Public Administration,Guangdong,China","institution_ids":["https://openalex.org/I4210103346"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101307582"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16428755,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9229000210762024,"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":0.9229000210762024,"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":0.03150000050663948,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.008299999870359898,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5702000260353088},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5004000067710876},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4120999872684479},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.40880000591278076},{"id":"https://openalex.org/keywords/natural-language-understanding","display_name":"Natural language understanding","score":0.33489999175071716},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.33230000734329224},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3199999928474426},{"id":"https://openalex.org/keywords/graph-property","display_name":"Graph property","score":0.3197000026702881}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7409999966621399},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5702000260353088},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5077999830245972},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5004000067710876},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.415800005197525},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.40880000591278076},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3499000072479248},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.33489999175071716},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C64339825","wikidata":"https://www.wikidata.org/wiki/Q722659","display_name":"Graph property","level":5,"score":0.3197000026702881},{"id":"https://openalex.org/C66945725","wikidata":"https://www.wikidata.org/wiki/Q18388823","display_name":"Text graph","level":3,"score":0.311599999666214},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C16311509","wikidata":"https://www.wikidata.org/wiki/Q4148050","display_name":"Dependency graph","level":3,"score":0.2628999948501587},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.2572999894618988}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11210233","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309612","display_name":"Natural Science Foundation of Shanghai","ror":null},{"id":"https://openalex.org/F4320334111","display_name":"Innovation Fund","ror":null},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2807150694","https://openalex.org/W4376864968","https://openalex.org/W4389519213","https://openalex.org/W4400524696","https://openalex.org/W4402915290"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,14,193],"Text-Attributed":[3],"Graphs":[4],"(TAGs)":[5],"have":[6],"attracted":[7],"significant":[8],"attention":[9],"for":[10,35],"their":[11],"wide-ranging":[12],"applications":[13],"domains":[15],"such":[16],"as":[17],"social":[18],"networks,":[19],"academics,":[20],"and":[21,75,84,136,162],"e-commerce.":[22],"The":[23],"powerful":[24],"text-processing":[25],"capabilities":[26],"of":[27,64,72,201],"pre-trained":[28,108],"language":[29,109],"models":[30],"offer":[31],"a":[32,61,97,146,186],"promising":[33],"avenue":[34],"effectively":[36],"integrating":[37],"textual":[38,73],"attributes":[39],"with":[40],"graph":[41,54,67,76,100,128,135,159,172],"structures.":[42],"However,":[43],"existing":[44],"methods":[45,192],"exhibit":[46],"two":[47],"key":[48],"limitations:":[49],"(1)":[50],"reliance":[51],"on":[52,157,174],"rigid":[53],"construction":[55,160],"processes":[56,161],"that":[57,115,182],"fail":[58],"to":[59,79,111,125,139],"capture":[60],"comprehensive":[62],"view":[63],"the":[65,107,117,121,127,134,142,155,190,199,202],"text-attributed":[66],"data;":[68],"(2)":[69],"insufficient":[70],"fusion":[71],"semantics":[74],"topology,":[77],"leading":[78],"information":[80,164],"loss,":[81],"unstable":[82],"training,":[83],"limited":[85],"generalization":[86],"across":[87,195],"diverse":[88],"downstream":[89],"tasks.":[90],"In":[91],"this":[92],"work,":[93],"we":[94],"propose":[95],"GA-Clip,":[96],"novel":[98],"semantic-aware":[99],"augmentation":[101,153],"contrastive":[102,149],"learning":[103,150,173],"model.":[104],"We":[105,130],"leverage":[106],"model":[110],"generate":[112],"semantic":[113],"edges":[114],"extract":[116],"fine-grained":[118],"topology":[119],"within":[120],"text":[122,137],"feature":[123],"space":[124],"augment":[126],"structure.":[129],"then":[131],"separately":[132],"employ":[133],"encoders":[138],"sufficiently":[140],"fuse":[141],"different":[143,166],"modalities":[144],"through":[145],"modified":[147],"self-supervised":[148],"approach.":[151],"This":[152],"mitigates":[154],"dependency":[156],"cumbersome":[158],"integrates":[163],"from":[165],"modalities,":[167],"which":[168],"jointly":[169],"enables":[170],"scalable":[171],"coarse-grained,":[175],"large-scale":[176],"source":[177],"data.":[178],"Experimental":[179],"results":[180],"demonstrated":[181],"our":[183],"approach":[184],"achieved":[185],"2-4%":[187],"improvement":[188],"over":[189],"SOTA":[191],"accuracy":[194],"multiple":[196],"datasets,":[197],"validating":[198],"effectiveness":[200],"proposed":[203],"method.":[204]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-30T00:00:00"}
