{"id":"https://openalex.org/W4312843444","doi":"https://doi.org/10.1109/tbdata.2022.3218128","title":"DAWN: Domain Generalization Based Network Alignment","display_name":"DAWN: Domain Generalization Based Network Alignment","publication_year":2022,"publication_date":"2022-11-04","ids":{"openalex":"https://openalex.org/W4312843444","doi":"https://doi.org/10.1109/tbdata.2022.3218128"},"language":"en","primary_location":{"id":"doi:10.1109/tbdata.2022.3218128","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2022.3218128","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Big Data","raw_type":"journal-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/A5031072441","display_name":"Shuai Gao","orcid":"https://orcid.org/0000-0003-3424-206X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuai Gao","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101759820","display_name":"Zhongbao Zhang","orcid":"https://orcid.org/0000-0002-3242-150X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongbao Zhang","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036865453","display_name":"Sen Su","orcid":"https://orcid.org/0000-0003-4266-7527"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sen Su","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5031072441"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":1.1032,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.81878287,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"9","issue":"3","first_page":"878","last_page":"888"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9984999895095825,"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":0.9984999895095825,"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":0.9907000064849854,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6681821942329407},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.5848691463470459},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5597295761108398},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5078349709510803},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.45469963550567627},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35271763801574707},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3445918560028076},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16394078731536865}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6681821942329407},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.5848691463470459},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5597295761108398},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5078349709510803},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.45469963550567627},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35271763801574707},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3445918560028076},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16394078731536865},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tbdata.2022.3218128","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2022.3218128","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Big Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5699999928474426}],"awards":[{"id":"https://openalex.org/G109947451","display_name":null,"funder_award_id":"U1936103","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2993257473","display_name":null,"funder_award_id":"61921003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W1613613084","https://openalex.org/W1888005072","https://openalex.org/W1980680715","https://openalex.org/W2058036501","https://openalex.org/W2075633077","https://openalex.org/W2201792562","https://openalex.org/W2374981910","https://openalex.org/W2537308293","https://openalex.org/W2571692900","https://openalex.org/W2572926828","https://openalex.org/W2598689838","https://openalex.org/W2614424296","https://openalex.org/W2615318713","https://openalex.org/W2788259657","https://openalex.org/W2793022729","https://openalex.org/W2896831016","https://openalex.org/W2896997271","https://openalex.org/W2904205492","https://openalex.org/W2914268208","https://openalex.org/W2952476201","https://openalex.org/W2963789515","https://openalex.org/W2964015378","https://openalex.org/W2964418074","https://openalex.org/W2985032289","https://openalex.org/W3006253006","https://openalex.org/W3013459733","https://openalex.org/W3046819722","https://openalex.org/W3046983129","https://openalex.org/W3085678997","https://openalex.org/W3089674223","https://openalex.org/W3098853476","https://openalex.org/W3105317915","https://openalex.org/W3142917731","https://openalex.org/W3153663385","https://openalex.org/W3156855620","https://openalex.org/W3166419968","https://openalex.org/W3169423864","https://openalex.org/W3170451879","https://openalex.org/W3171051766","https://openalex.org/W3189951784","https://openalex.org/W3211009556","https://openalex.org/W4286559488","https://openalex.org/W4287728573","https://openalex.org/W4288287305","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W4298289240","https://openalex.org/W6637568146","https://openalex.org/W6726873649","https://openalex.org/W6731581786","https://openalex.org/W6731785350","https://openalex.org/W6738964360","https://openalex.org/W6745537798","https://openalex.org/W6765285020","https://openalex.org/W6766417161","https://openalex.org/W6773795876","https://openalex.org/W6774827837","https://openalex.org/W6780233385","https://openalex.org/W6787888564","https://openalex.org/W6789380839"],"related_works":["https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W3162204513","https://openalex.org/W2371138613","https://openalex.org/W2048963458","https://openalex.org/W43109613","https://openalex.org/W2359952343","https://openalex.org/W2239445980","https://openalex.org/W2080152487","https://openalex.org/W3083152911"],"abstract_inverted_index":{"Network":[0],"alignment":[1,19,53,90,163,177],"aims":[2],"to":[3,10,92,152,174],"discover":[4],"nodes":[5],"in":[6,25,45,97,105,137],"different":[7],"networks":[8,39],"belonging":[9],"the":[11,17,31,36,52,61,70,88,98,185,207],"same":[12],"identity.":[13],"In":[14],"recent":[15],"years,":[16],"network":[18,63,162],"problem":[20],"has":[21],"aroused":[22],"significant":[23],"attentions":[24],"both":[26],"industry":[27],"and":[28,44,69,72,80,170,190,201],"academia.":[29],"With":[30],"rapid":[32],"growth":[33],"of":[34,38,54,188],"information,":[35],"sizes":[37],"are":[40,75],"usually":[41],"very":[42],"large":[43],"most":[46],"cases":[47],"we":[48,108,139,157,180],"only":[49],"focus":[50],"on":[51,184],"partial":[55],"networks.":[56],"However,":[57],"under":[58],"this":[59,103,106],"circumstances,":[60],"collected":[62],"data":[64,74],"may":[65],"be":[66],"highly":[67],"biased,":[68],"training":[71],"testing":[73],"no":[76],"longer":[77],"i.i.d.":[78],"(identically":[79],"independently":[81],"distributed).":[82],"Thus,":[83],"it":[84],"is":[85],"difficult":[86],"for":[87],"trained":[89],"model":[91,147,164],"have":[93],"a":[94,110,142,159],"good":[95],"performance":[96],"test":[99],"set.":[100],"To":[101],"bridge":[102],"gap,":[104],"paper,":[107],"propose":[109],"novel":[111,143,160],"<underline":[112,117,123,128],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[113,118,124,129],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">D</u>":[114],"omain":[115],"gener":[116],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">A</u>":[119],"lization":[120],"based":[121],"net":[122],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">W</u>":[125],"ork":[126],"alig":[127],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">N</u>":[130],"ment":[131],"approach":[132],"termed":[133],"as":[134],"DAWN.":[135],"Specifically,":[136],"DAWN,":[138],"first":[140],"design":[141,158],"invariant":[144,161],"feature":[145],"extraction":[146],"which":[148,165],"leverages":[149],"adversarial":[150],"learning":[151],"extract":[153],"domain-invariant":[154,176],"features.":[155],"Then,":[156],"can":[166,195],"achieve":[167,197],"global":[168],"optimum":[169,172],"local":[171],"simultaneously":[173],"learn":[175],"patterns.":[178],"Finally,":[179],"conduct":[181],"extensive":[182],"experiments":[183],"benchmark":[186],"dataset":[187],"Facebook-Twitter,":[189],"results":[191],"show":[192],"that":[193],"DAWN":[194],"averagely":[196],"14.01%":[198],"higher":[199,203],"Hits@k":[200],"10.63%":[202],"MRR@k":[204],"compared":[205],"with":[206],"state-of-the-art":[208],"methods.":[209]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}