{"id":"https://openalex.org/W7137880081","doi":"https://doi.org/10.1609/aaai.v40i28.39547","title":"Causally-Aware Attribute Completion for Incomplete Federated Graph Clustering","display_name":"Causally-Aware Attribute Completion for Incomplete Federated Graph Clustering","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7137880081","doi":"https://doi.org/10.1609/aaai.v40i28.39547"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i28.39547","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i28.39547","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i28.39547","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129664050","display_name":"Jingxin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jingxin Liu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085607536","display_name":"Wenxuan Tu","orcid":"https://orcid.org/0000-0002-1353-2968"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenxuan Tu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129727032","display_name":"Haotian Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haotian Wang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129749572","display_name":"Renda Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Renda Han","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129688335","display_name":"Haoyi Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haoyi Li","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129682101","display_name":"Junlong Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junlong Wu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073355834","display_name":"Xiangyan Tang","orcid":"https://orcid.org/0000-0002-3874-8415"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiangyan Tang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5129664050"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09684323,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"28","first_page":"23730","last_page":"23738"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.84579998254776,"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":0.84579998254776,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.06199999898672104,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.014100000262260437,"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/cluster-analysis","display_name":"Cluster analysis","score":0.6176000237464905},{"id":"https://openalex.org/keywords/upload","display_name":"Upload","score":0.5638999938964844},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.536899983882904},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.44999998807907104},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3693000078201294},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.3691999912261963},{"id":"https://openalex.org/keywords/clustering-coefficient","display_name":"Clustering coefficient","score":0.35190001130104065}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7513999938964844},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6176000237464905},{"id":"https://openalex.org/C71901391","wikidata":"https://www.wikidata.org/wiki/Q7126699","display_name":"Upload","level":2,"score":0.5638999938964844},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.536899983882904},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5224999785423279},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.44999998807907104},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.35190001130104065},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.34060001373291016},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3375000059604645},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3328000009059906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3305000066757202},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C131992880","wikidata":"https://www.wikidata.org/wiki/Q2528185","display_name":"Subgraph isomorphism problem","level":3,"score":0.30239999294281006},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.2696000039577484},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i28.39547","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i28.39547","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i28.39547","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i28.39547","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Node-level":[0],"federated":[1],"graph":[2],"clustering":[3],"allows":[4],"multiple":[5,138],"unlabeled":[6],"subgraph":[7,166],"holders":[8],"to":[9,48,60,101,120,132,147,153,194,204,207],"collaboratively":[10],"train":[11],"on":[12,179,217],"node-level":[13],"tasks":[14],"without":[15],"sharing":[16],"private":[17],"information.":[18],"Existing":[19],"methods":[20],"usually":[21],"assume":[22],"that":[23,97],"the":[24,36,70,110,117,122,133,160,180,205,222],"node":[25],"attributes":[26],"are":[27,186],"complete":[28],"and":[29,128,174,224],"have":[30],"achieved":[31],"promising":[32],"progress.":[33],"However,":[34],"in":[35,50,109],"Federated":[37,86],"Graph":[38,87,169],"Learning":[39],"(FGL)":[40],"scenarios,":[41],"this":[42,76],"assumption":[43],"is":[44,201],"overly":[45],"strict":[46],"due":[47],"failures":[49],"data":[51],"collection":[52],"devices.":[53],"Consequently,":[54],"most":[55],"existing":[56],"FGL":[57,192],"frameworks":[58],"struggle":[59],"extract":[61,121],"useful":[62],"features":[63],"from":[64,164],"attribute-incomplete":[65],"graphs":[66],"for":[67,84,105,212],"clustering,":[68,156],"yet":[69],"issue":[71],"remains":[72],"underexplored.":[73],"To":[74],"bridge":[75],"gap,":[77],"we":[78,114,136,157],"propose":[79],"a":[80,92,143,168,190,196],"causally-aware":[81],"attribute":[82,103,111,150,210],"completion":[83,104,112,211],"Incomplete":[85],"Clustering":[88],"(IFedGC),":[89],"which":[90,200],"constructs":[91],"reliable":[93,155],"global":[94,144],"causal":[95,118,123,140,145,198],"structure":[96,119],"incorporates":[98],"clustering-friendly":[99],"information":[100],"guide":[102],"each":[106,165],"subgraph.":[107],"Specifically,":[108],"step,":[113],"first":[115,158],"construct":[116],"relationships":[124],"between":[125],"initialized":[126],"features,":[127],"then":[129],"upload":[130],"them":[131],"server.":[134,181],"Subsequently,":[135],"integrate":[137],"uploaded":[139],"structures":[141],"into":[142,189],"one":[146],"achieve":[148],"cross-client":[149],"completion.":[151],"Moreover,":[152],"support":[154],"collect":[159],"high-confidence":[161],"cluster":[162],"centroids":[163,178],"using":[167],"Neural":[170],"Network":[171],"(GNN)":[172],"model":[173],"subsequently":[175],"aggregate":[176],"these":[177],"The":[182],"above":[183],"two":[184],"steps":[185],"seamlessly":[187],"integrated":[188],"unified":[191],"framework":[193],"obtain":[195],"clustering-oriented":[197],"structure,":[199],"sent":[202],"back":[203],"client":[206],"promote":[208],"high-quality":[209],"better":[213],"clustering.":[214],"Extensive":[215],"results":[216],"five":[218],"benchmark":[219],"datasets":[220],"demonstrate":[221],"effectiveness":[223],"superiority":[225],"of":[226],"IFedGC":[227],"against":[228],"its":[229],"competitors.":[230]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}