{"id":"https://openalex.org/W4411551270","doi":"https://doi.org/10.1109/cscwd64889.2025.11033485","title":"Social Network Classification Based on Adaptive Structure-Alignment Federated Graph Learning Framework","display_name":"Social Network Classification Based on Adaptive Structure-Alignment Federated Graph Learning Framework","publication_year":2025,"publication_date":"2025-05-05","ids":{"openalex":"https://openalex.org/W4411551270","doi":"https://doi.org/10.1109/cscwd64889.2025.11033485"},"language":"en","primary_location":{"id":"doi:10.1109/cscwd64889.2025.11033485","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd64889.2025.11033485","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","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/A5092108813","display_name":"Boyang Liu","orcid":"https://orcid.org/0000-0002-0814-002X"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Boyang Liu","raw_affiliation_strings":["School of Software Engineering, Xi'an Jiaotong University,Xi'an,China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, Xi'an Jiaotong University,Xi'an,China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100508946","display_name":"Lin Liu","orcid":"https://orcid.org/0009-0003-5356-9014"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Liu","raw_affiliation_strings":["School of Software Engineering, Xi'an Jiaotong University,Xi'an,China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, Xi'an Jiaotong University,Xi'an,China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100455048","display_name":"Chenxu Wang","orcid":"https://orcid.org/0000-0002-9539-5046"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenxu Wang","raw_affiliation_strings":["Xi'an Jiaotong University,MOE KLINNS Lab,Xi'an,China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University,MOE KLINNS Lab,Xi'an,China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5092108813"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07802679,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"854","last_page":"859"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.906000018119812,"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":0.906000018119812,"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":0.7961459159851074},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.48950064182281494},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43074730038642883},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.414900004863739},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35325300693511963},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34680309891700745}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7961459159851074},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.48950064182281494},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43074730038642883},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.414900004863739},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35325300693511963},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34680309891700745},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cscwd64889.2025.11033485","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd64889.2025.11033485","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2964015378","https://openalex.org/W3123459983","https://openalex.org/W3178875373","https://openalex.org/W3182158470","https://openalex.org/W4224330113","https://openalex.org/W4281753687","https://openalex.org/W4296126761","https://openalex.org/W4298371664","https://openalex.org/W4311080353","https://openalex.org/W4318586086","https://openalex.org/W4318822841","https://openalex.org/W4366463843","https://openalex.org/W4382202967","https://openalex.org/W4382318655","https://openalex.org/W4385221361","https://openalex.org/W4387641291","https://openalex.org/W4390480779","https://openalex.org/W4392901776","https://openalex.org/W4393156671","https://openalex.org/W4401863295","https://openalex.org/W6745537798","https://openalex.org/W6752029299","https://openalex.org/W6759238902","https://openalex.org/W6774120287","https://openalex.org/W6797220997","https://openalex.org/W6838746084"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Federated":[0],"learning":[1,70,81,95,121],"plays":[2],"a":[3,125,159],"crucial":[4],"role":[5],"in":[6,68,158,172],"social":[7,168],"network":[8,169],"classification.":[9],"By":[10],"performing":[11],"model":[12,67],"training":[13],"on":[14],"local":[15,47,50,142],"devices":[16],"and":[17,40,56,85,104,136,171],"sharing":[18,105,140],"only":[19,24],"parameter":[20],"updates,":[21],"it":[22],"not":[23],"enhances":[25],"the":[26,30,42,46,58,66,102,153,177,182,187],"generalization":[27,192],"ability":[28],"of":[29,45,106,179],"model,":[31],"but":[32],"also":[33,184],"provides":[34],"support":[35],"for":[36,65],"personalized":[37,160],"recommendation":[38],"systems":[39],"protects":[41],"data":[43,90,135],"privacy":[44],"clients.":[48,77,111],"However,":[49],"clients":[51],"usually":[52],"have":[53,83],"incomplete":[54],"graphs":[55],"misrepresent":[57],"graph":[59,73,80,89,94,120,134],"structure.":[60],"Structural":[61],"heterogeneity":[62],"poses":[63],"challenges":[64],"effectively":[69],"significant":[71],"similarity":[72],"patterns":[74],"among":[75,110,150],"different":[76],"Existing":[78],"federated":[79,93,119,138],"methods":[82,96],"limitations":[84],"cannot":[86],"handle":[87],"Non-IID":[88,174],"well,":[91],"most":[92],"aggregate":[97],"all":[98],"learnable":[99],"parameters,":[100],"complicating":[101],"extraction":[103],"specific":[107],"structural":[108,130],"knowledge":[109,139,155],"Therefore,":[112],"this":[113],"paper":[114],"proposes":[115],"an":[116],"adaptive":[117],"structure-alignment":[118],"framework(ASAFGL),":[122],"which":[123],"utilizes":[124],"structure":[126,145],"encoder":[127,146],"to":[128],"separate":[129],"information":[131],"from":[132],"heterogeneous":[133],"realize":[137],"during":[141],"training.":[143],"The":[144],"is":[147,156],"uniformly":[148],"shared":[149],"clients,":[151],"whereas":[152],"feature-based":[154],"acquired":[157],"manner.":[161],"We":[162],"conduct":[163],"comprehensive":[164],"experiments":[165],"across":[166],"various":[167],"datasets":[170],"cross-dataset":[173],"settings,":[175],"showcasing":[176],"superiority":[178],"ASAFGL.":[180],"Furthermore,":[181],"results":[183],"show":[185],"that":[186],"framework":[188],"has":[189],"good":[190],"cross-domain":[191],"ability.":[193]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}