{"id":"https://openalex.org/W4306317785","doi":"https://doi.org/10.1145/3511808.3557605","title":"GradAlign+: Empowering Gradual Network Alignment Using Attribute Augmentation","display_name":"GradAlign+: Empowering Gradual Network Alignment Using Attribute Augmentation","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4306317785","doi":"https://doi.org/10.1145/3511808.3557605"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557605","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557605","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","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/A5012734069","display_name":"Jin-Duk Park","orcid":"https://orcid.org/0000-0002-7906-8475"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jin-Duk Park","raw_affiliation_strings":["Yonsei University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067894717","display_name":"Cong Tran","orcid":"https://orcid.org/0000-0001-9467-4978"},"institutions":[{"id":"https://openalex.org/I4210095603","display_name":"Vietnam Posts and Telecommunications Group (Vietnam)","ror":"https://ror.org/00q0e7f94","country_code":"VN","type":"company","lineage":["https://openalex.org/I4210095603"]},{"id":"https://openalex.org/I4400600977","display_name":"Posts and Telecommunications Institute of Technology","ror":"https://ror.org/0363rtq22","country_code":null,"type":"education","lineage":["https://openalex.org/I4400600977"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Cong Tran","raw_affiliation_strings":["Posts and Telecommunications Institute of Technology, Hanoi, Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Posts and Telecommunications Institute of Technology, Hanoi, Vietnam","institution_ids":["https://openalex.org/I4210095603","https://openalex.org/I4400600977"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086873475","display_name":"Won-Yong Shin","orcid":"https://orcid.org/0000-0002-6533-3469"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Won-Yong Shin","raw_affiliation_strings":["Yonsei University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089964993","display_name":"Xin Cao","orcid":"https://orcid.org/0000-0002-3519-7013"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xin Cao","raw_affiliation_strings":["The University of New South Wales, Sydney, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of New South Wales, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I31746571"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.687,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.8673288,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4374","last_page":"4378"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9962000250816345,"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"}},{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9789999723434448,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.7066508531570435},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7006382346153259},{"id":"https://openalex.org/keywords/centrality","display_name":"Centrality","score":0.6703583598136902},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5522635579109192},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5195267796516418},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5103709101676941},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.501366376876831},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4921559691429138},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4912986159324646},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.41699880361557007},{"id":"https://openalex.org/keywords/attention-network","display_name":"Attention network","score":0.4105425477027893},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3971092998981476},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30185526609420776},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1567145586013794},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09137624502182007},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08522063493728638},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.08211159706115723}],"concepts":[{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.7066508531570435},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7006382346153259},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.6703583598136902},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5522635579109192},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5195267796516418},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5103709101676941},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.501366376876831},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4921559691429138},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4912986159324646},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.41699880361557007},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.4105425477027893},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3971092998981476},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30185526609420776},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1567145586013794},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09137624502182007},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08522063493728638},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.08211159706115723},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3511808.3557605","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557605","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3949046853","display_name":null,"funder_award_id":"2021R1A2C3004345","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G7615543113","display_name":"Efficient and effective location-aware search on social networks","funder_award_id":"DE190100663","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1597910678","https://openalex.org/W1973956316","https://openalex.org/W2026417691","https://openalex.org/W2059975159","https://openalex.org/W2070553571","https://openalex.org/W2289831356","https://openalex.org/W2294347342","https://openalex.org/W2391555403","https://openalex.org/W2514012150","https://openalex.org/W2585895356","https://openalex.org/W2793022729","https://openalex.org/W2888657195","https://openalex.org/W2911702602","https://openalex.org/W2962975498","https://openalex.org/W3030424116","https://openalex.org/W3030734823","https://openalex.org/W3072176192","https://openalex.org/W3103148916","https://openalex.org/W4283790743"],"related_works":["https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2912814903","https://openalex.org/W2123605750","https://openalex.org/W2088740331","https://openalex.org/W3038102983","https://openalex.org/W2950907416","https://openalex.org/W1559483280","https://openalex.org/W2082479932","https://openalex.org/W2932872266"],"abstract_inverted_index":{"Network":[0],"alignment":[1],"(NA)":[2],"is":[3,28,59,70,98],"the":[4,63,78,101,128,149,174],"task":[5],"of":[6,23,65,87,100,168,176],"discovering":[7,137],"node":[8,36,55,88,92,107,130,138],"correspondences":[9],"across":[10],"different":[11],"networks.":[12],"Although":[13],"NA":[14,52,76,163],"methods":[15],"have":[16],"achieved":[17],"remarkable":[18],"success":[19],"in":[20],"a":[21,50,73,85,122],"myriad":[22],"scenarios,":[24],"their":[25],"satisfactory":[26],"performance":[27],"not":[29,40],"without":[30],"prior":[31],"anchor":[32],"link":[33],"information":[34],"and/or":[35],"attributes,":[37],"which":[38,127],"may":[39],"always":[41],"be":[42],"available.":[43],"In":[44],"this":[45],"paper,":[46],"we":[47],"propose":[48],"Grad-Align+,":[49],"novel":[51],"method":[53],"using":[54],"attribute":[56,178],"augmentation":[57,179],"that":[58,81,156],"quite":[60],"robust":[61],"to":[62,148],"absence":[64],"such":[66],"additional":[67],"information.":[68],"Grad-Align+":[69,97,157],"built":[71],"upon":[72],"recent":[74],"state-of-the-art":[75],"method,":[77],"so-called":[79],"Grad-Align,":[80],"gradually":[82,136],"discovers":[83],"only":[84],"part":[86],"pairs":[89,93,139],"until":[90],"all":[91],"are":[94,132],"found.":[95],"Specifically,":[96],"composed":[99],"following":[102],"key":[103],"components:":[104],"1)":[105],"augmenting":[106],"attributes":[108,131],"based":[109],"on":[110],"nodes'":[111],"centrality":[112],"measures,":[113],"2)":[114],"calculating":[115,141],"an":[116],"embedding":[117],"similarity":[118],"matrix":[119],"extracted":[120],"from":[121],"graph":[123],"neural":[124],"network":[125],"into":[126],"augmented":[129],"fed,":[133],"and":[134,172],"3)":[135],"by":[140],"similarities":[142],"between":[143],"cross-network":[144,151],"nodes":[145],"with":[146],"respect":[147],"aligned":[150],"neighbor-pair.":[152],"Experimental":[153],"results":[154],"demonstrate":[155],"exhibits":[158],"(a)":[159],"superiority":[160],"over":[161],"benchmark":[162],"methods,":[164],"(b)":[165],"empirical":[166],"validation":[167],"our":[169,177],"theoretical":[170],"findings,":[171],"(c)":[173],"effectiveness":[175],"module.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
