{"id":"https://openalex.org/W4223561032","doi":"https://doi.org/10.48550/arxiv.2204.05104","title":"Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation","display_name":"Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation","publication_year":2022,"publication_date":"2022-04-08","ids":{"openalex":"https://openalex.org/W4223561032","doi":"https://doi.org/10.48550/arxiv.2204.05104"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2204.05104","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.05104","pdf_url":"https://arxiv.org/pdf/2204.05104","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2204.05104","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100603554","display_name":"Jin Yuan","orcid":"https://orcid.org/0000-0002-5803-6626"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yuan, Jin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101488580","display_name":"Feng Hou","orcid":"https://orcid.org/0000-0001-9660-8034"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hou, Feng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069959549","display_name":"Yangzhou Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Yangzhou","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101873354","display_name":"Zhongchao Shi","orcid":"https://orcid.org/0000-0002-5216-3827"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Zhongchao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083059745","display_name":"Xin Geng","orcid":"https://orcid.org/0000-0001-6377-3308"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Geng, Xin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100728564","display_name":"Jianping Fan","orcid":"https://orcid.org/0000-0002-4923-0910"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Jianping","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100728762","display_name":"Yong Rui","orcid":"https://orcid.org/0000-0002-9142-5914"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rui, Yong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5100603554"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.995199978351593,"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":0.995199978351593,"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":0.9251000285148621,"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/computer-science","display_name":"Computer science","score":0.7585699558258057},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6155232787132263},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5005025863647461},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4962218403816223},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.47483688592910767},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4631492495536804},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4517093598842621},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4301101863384247},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.42934221029281616},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.20001158118247986}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7585699558258057},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6155232787132263},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5005025863647461},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4962218403816223},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.47483688592910767},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4631492495536804},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4517093598842621},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4301101863384247},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.42934221029281616},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.20001158118247986},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2204.05104","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.05104","pdf_url":"https://arxiv.org/pdf/2204.05104","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2204.05104","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2204.05104","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2204.05104","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.05104","pdf_url":"https://arxiv.org/pdf/2204.05104","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4223561032.pdf","grobid_xml":"https://content.openalex.org/works/W4223561032.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2997567050","https://openalex.org/W1483272040","https://openalex.org/W4283377908","https://openalex.org/W1533421371","https://openalex.org/W2003050223","https://openalex.org/W2787993192","https://openalex.org/W2091777911","https://openalex.org/W2766405861","https://openalex.org/W2360975119","https://openalex.org/W2912421143"],"abstract_inverted_index":{"Domain":[0],"adaptation":[1,26,63,220],"(DA)":[2],"tries":[3],"to":[4,71,75,96,105,115,145,176,197],"tackle":[5],"the":[6,9,16,20,89,97,112,116,121,128,132,174,225],"scenarios":[7],"when":[8,120],"test":[10],"data":[11,74],"does":[12],"not":[13,93],"fully":[14],"follow":[15],"same":[17,122],"distribution":[18],"of":[19,227],"training":[21],"data,":[22],"and":[23,60,131,135,150,183],"multi-source":[24,61,218],"domain":[25,62,200,219],"(MSDA)":[27],"is":[28,53,125,143,171,189],"very":[29],"attractive":[30],"for":[31],"real":[32],"world":[33],"applications.":[34],"By":[35],"learning":[36,42,59,84],"from":[37,111,232],"large-scale":[38],"unlabeled":[39,73],"samples,":[40],"self-supervised":[41,58,83],"has":[43,212],"now":[44],"become":[45],"a":[46,65,159,167,193],"new":[47],"trend":[48],"in":[49],"deep":[50],"learning.":[51],"It":[52],"worth":[54],"noting":[55],"that":[56,207],"both":[57,69],"share":[64],"similar":[66],"goal:":[67],"they":[68],"aim":[70],"leverage":[72],"learn":[76,106],"more":[77,178],"expressive":[78,187],"representations.":[79],"Unfortunately,":[80],"traditional":[81],"multi-task":[82],"faces":[85],"two":[86],"challenges:":[87],"(1)":[88],"pretext":[90,113,129],"task":[91,114,130],"may":[92],"strongly":[94],"relate":[95],"downstream":[98,133],"task,":[99],"thus":[100],"it":[101,142],"could":[102],"be":[103],"difficult":[104],"useful":[107],"knowledge":[108,151,184],"being":[109],"shared":[110,126],"target":[117],"task;":[118],"(2)":[119],"feature":[123],"extractor":[124],"between":[127],"one":[134],"only":[136],"different":[137,233],"prediction":[138],"heads":[139],"are":[140],"used,":[141],"ineffective":[144],"enable":[146,177],"inter-task":[147,180],"information":[148,181],"exchange":[149,182],"sharing.":[152,185],"To":[153],"address":[154],"these":[155],"issues,":[156],"we":[157],"propose":[158],"novel":[160],"\\textbf{S}elf-\\textbf{S}upervised":[161],"\\textbf{G}raph":[162],"Neural":[163],"Network":[164],"(SSG),":[165],"where":[166],"graph":[168],"neural":[169],"network":[170],"used":[172],"as":[173],"bridge":[175],"effective":[179],"More":[186],"representation":[188],"learned":[190],"by":[191],"adopting":[192],"mask":[194,198],"token":[195],"strategy":[196],"some":[199],"information.":[201],"Our":[202],"extensive":[203],"experiments":[204],"have":[205,223],"demonstrated":[206],"our":[208,228],"proposed":[209,229],"SSG":[210,230],"method":[211,231],"achieved":[213],"state-of-the-art":[214],"results":[215],"over":[216],"four":[217],"datasets,":[221],"which":[222],"shown":[224],"effectiveness":[226],"aspects.":[234]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
