{"id":"https://openalex.org/W4387969518","doi":"https://doi.org/10.1145/3581783.3612264","title":"SA-GDA: Spectral Augmentation for Graph Domain Adaptation","display_name":"SA-GDA: Spectral Augmentation for Graph Domain Adaptation","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4387969518","doi":"https://doi.org/10.1145/3581783.3612264"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3612264","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612264","pdf_url":null,"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":"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/A5018094819","display_name":"Jinhui Pang","orcid":"https://orcid.org/0000-0002-7946-7089"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinhui Pang","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103206670","display_name":"Zixuan Wang","orcid":"https://orcid.org/0009-0000-2380-3832"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zixuan Wang","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040639891","display_name":"Jiliang Tang","orcid":"https://orcid.org/0000-0001-7125-3898"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiliang Tang","raw_affiliation_strings":["Michigan State University, East Lansing, MI, USA"],"affiliations":[{"raw_affiliation_string":"Michigan State University, East Lansing, MI, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027077879","display_name":"Mingyan Xiao","orcid":"https://orcid.org/0000-0002-6184-355X"},"institutions":[{"id":"https://openalex.org/I98947143","display_name":"California State Polytechnic University","ror":"https://ror.org/05by5hm18","country_code":"US","type":"education","lineage":["https://openalex.org/I98947143"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingyan Xiao","raw_affiliation_strings":["California State Polytechnic University, Pomona, Pomona, CA, USA"],"affiliations":[{"raw_affiliation_string":"California State Polytechnic University, Pomona, Pomona, CA, USA","institution_ids":["https://openalex.org/I98947143"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027852193","display_name":"Nan Yin","orcid":"https://orcid.org/0000-0002-0177-8514"},"institutions":[{"id":"https://openalex.org/I4210113480","display_name":"Mohamed bin Zayed University of Artificial Intelligence","ror":"https://ror.org/0258gkt32","country_code":"AE","type":"education","lineage":["https://openalex.org/I4210113480"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Nan Yin","raw_affiliation_strings":["Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE"],"affiliations":[{"raw_affiliation_string":"Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE","institution_ids":["https://openalex.org/I4210113480"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5018094819"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":2.0739,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.89715661,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"309","last_page":"318"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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":0.9997000098228455,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.984499990940094,"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":0.9771000146865845,"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":0.7278350591659546},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6482328176498413},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5677180290222168},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5353766679763794},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4977405369281769},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.46714019775390625},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.4523882567882538},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4359186291694641},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4183819890022278},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3770264983177185},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3667580485343933}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7278350591659546},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6482328176498413},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5677180290222168},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5353766679763794},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4977405369281769},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.46714019775390625},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.4523882567882538},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4359186291694641},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4183819890022278},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3770264983177185},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3667580485343933},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3612264","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612264","pdf_url":null,"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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W2022322548","https://openalex.org/W2101491865","https://openalex.org/W2154851992","https://openalex.org/W2187089797","https://openalex.org/W2320990637","https://openalex.org/W2895281799","https://openalex.org/W2907492528","https://openalex.org/W2943959761","https://openalex.org/W2964288524","https://openalex.org/W2997964288","https://openalex.org/W2998115938","https://openalex.org/W3012644407","https://openalex.org/W3041085747","https://openalex.org/W3092609815","https://openalex.org/W3098465726","https://openalex.org/W3104097132","https://openalex.org/W3128443161","https://openalex.org/W3176285518","https://openalex.org/W3177377177","https://openalex.org/W3207311065","https://openalex.org/W4206229730","https://openalex.org/W4221166060","https://openalex.org/W4288073539","https://openalex.org/W4294152625","https://openalex.org/W4321851522","https://openalex.org/W6600339963","https://openalex.org/W6603251197"],"related_works":["https://openalex.org/W3183901164","https://openalex.org/W3135818718","https://openalex.org/W4290188444","https://openalex.org/W3080655457","https://openalex.org/W3167935049","https://openalex.org/W3003905048","https://openalex.org/W2253429366","https://openalex.org/W3127975138","https://openalex.org/W4297577100","https://openalex.org/W3166286441"],"abstract_inverted_index":{"Graph":[0,121],"neural":[1],"networks":[2],"(GNNs)":[3],"have":[4],"achieved":[5],"impressive":[6],"impressions":[7],"for":[8,47,120,125,201],"graph-related":[9],"tasks.":[10],"However,":[11,84],"most":[12],"GNNs":[13],"are":[14,40,151],"primarily":[15],"studied":[16],"under":[17],"the":[18,56,60,67,81,87,92,108,117,135,145,155,159,167,173,181,233],"cases":[19],"of":[20,59,78,89,91,101,163,171,183,228,235],"signal":[21],"domain":[22,45,169,208,221],"with":[23,134,210],"supervised":[24],"training,":[25],"which":[26,73,106],"requires":[27],"abundant":[28],"task-specific":[29],"labels":[30,90],"and":[31,62,177,198],"is":[32],"difficult":[33],"to":[34,36,76,86,194,215],"transfer":[35,218],"other":[37],"domains.":[38],"There":[39],"few":[41],"works":[42],"focused":[43,53],"on":[44,54,225],"adaptation":[46],"graph":[48,126,191],"node":[49,127],"classification.":[50,128],"They":[51],"mainly":[52],"aligning":[55,172],"feature":[57,68,161,202],"space":[58,162],"source":[61],"target":[63,82,93],"domains,":[64,105],"without":[65],"considering":[66],"alignment":[69,100],"between":[70,219],"different":[71,104,139,149,164,220],"categories,":[72],"may":[74],"lead":[75],"confusion":[77],"classification":[79],"in":[80,138,144,166],"domain.":[83],"due":[85],"scarcity":[88],"domain,":[94,147],"we":[95,115,130,157,178,187,205],"cannot":[96],"directly":[97],"perform":[98],"effective":[99],"categories":[102],"from":[103],"makes":[107],"problem":[109],"more":[110],"challenging.":[111],"In":[112],"this":[113],"paper,":[114],"present":[116],"Spectral":[118],"Augmentation":[119],"Domain":[122],"Adaptation":[123],"(SA-GDA)":[124],"First,":[129],"observe":[131],"that":[132],"nodes":[133],"same":[136],"category":[137,160],"domains":[140,165],"exhibit":[141],"similar":[142],"characteristics":[143],"spectral":[146,168],"while":[148],"classes":[150],"quite":[152],"different.":[153],"Following":[154],"observation,":[156],"align":[158],"instead":[170],"whole":[174],"features":[175],"space,":[176],"theoretical":[179],"proof":[180],"stability":[182],"proposed":[184],"SA-GDA.":[185,237],"Then,":[186],"develop":[188],"a":[189,207,226],"dual":[190],"convolutional":[192],"network":[193],"jointly":[195],"exploits":[196],"local":[197],"global":[199],"consistency":[200],"aggregation.":[203],"Last,":[204],"utilize":[206],"classifier":[209],"an":[211],"adversarial":[212],"learning":[213],"submodule":[214],"facilitate":[216],"knowledge":[217],"graphs.":[222],"Experimental":[223],"results":[224],"variety":[227],"publicly":[229],"available":[230],"datasets":[231],"reveal":[232],"effectiveness":[234],"our":[236]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
