{"id":"https://openalex.org/W7223054664","doi":"https://doi.org/10.48550/arxiv.2610.25997","title":"Federated Community Detection in Bipartite Networks","display_name":"Federated Community Detection in Bipartite Networks","publication_year":2026,"publication_date":"2026-10-19","ids":{"openalex":"https://openalex.org/W7223054664","doi":"https://doi.org/10.48550/arxiv.2610.25997"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.25997","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.25997","pdf_url":"https://arxiv.org/pdf/2610.25997","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.25997","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135530805","display_name":"Wanjie Wang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Wang, Wanjie","raw_affiliation_strings":["Department of Statistics and Data Science , National University of Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Data Science , National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5135530805"],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.951200008392334,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.951200008392334,"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":0.006800000090152025,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.004699999932199717,"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/federated-learning","display_name":"federated learning","score":0.8799999952316284},{"id":"https://openalex.org/keywords/community-detection","display_name":"community detection","score":0.8659999966621399},{"id":"https://openalex.org/keywords/bipartite-graphs","display_name":"bipartite graphs","score":0.9990000128746033},{"id":"https://openalex.org/keywords/spectral-estimation","display_name":"spectral estimation","score":0.7929999828338623},{"id":"https://openalex.org/keywords/power-iteration","display_name":"power iteration","score":0.8830000162124634},{"id":"https://openalex.org/keywords/flicker","display_name":"flicker","score":0.8169999718666077},{"id":"https://openalex.org/keywords/stochastic-block-model","display_name":"stochastic block model","score":0.675000011920929},{"id":"https://openalex.org/keywords/non-asymptotic-convergence","display_name":"non-asymptotic convergence","score":0.8309999704360962},{"id":"https://openalex.org/keywords/matrix-vector-multiplication","display_name":"matrix-vector multiplication","score":0.6449999809265137},{"id":"https://openalex.org/keywords/projected-gradient-descent","display_name":"projected gradient descent","score":0.4659999907016754}],"concepts":[{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.6643999814987183},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.6304000020027161},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6266999840736389},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5985999703407288},{"id":"https://openalex.org/C2779982251","wikidata":"https://www.wikidata.org/wiki/Q25053762","display_name":"Stochastic block model","level":3,"score":0.5853000283241272},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5688999891281128},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5246000289916992},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.43470001220703125},{"id":"https://openalex.org/C109282560","wikidata":"https://www.wikidata.org/wiki/Q4166054","display_name":"Singular value","level":3,"score":0.4041000008583069},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38920000195503235},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.38350000977516174},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3643999993801117},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3617999851703644},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.3522999882698059},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28209999203681946},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C2985906921","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Spectral properties","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.25600001215934753}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.25997","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.25997","pdf_url":"https://arxiv.org/pdf/2610.25997","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.25997","doi":"https://doi.org/10.48550/arxiv.2610.25997","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.25997","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.25997","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.25997","pdf_url":"https://arxiv.org/pdf/2610.25997","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322724","display_name":"Ministry of Education, India","ror":"https://ror.org/048xjjh50"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7223054664.pdf","grobid_xml":"https://content.openalex.org/works/W7223054664.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W4399913007"],"related_works":["https://openalex.org/W4389821170","https://openalex.org/W3205950175","https://openalex.org/W6948302371","https://openalex.org/W7212521975","https://openalex.org/W2593320540","https://openalex.org/W4404682178","https://openalex.org/W3088243369","https://openalex.org/W7220692750","https://openalex.org/W2964146010","https://openalex.org/W4379379817"],"abstract_inverted_index":{"We":[0,19,55],"study":[1],"federated":[2,77,150],"spectral":[3,36,67,142,151],"estimation":[4],"when":[5,153],"a":[6,22,29,34,62,85,96,111,123,160],"large":[7],"matrix":[8],"is":[9,145],"horizontally":[10],"partitioned":[11,165],"across":[12],"servers":[13],"that":[14,27,104,119,136],"cannot":[15],"share":[16],"raw":[17],"data.":[18],"propose":[20],"FLICKER,":[21,72],"target-specific":[23],"gradient-based":[24],"power":[25],"iteration":[26],"recovers":[28],"singular":[30,93,108],"direction":[31],"associated":[32],"with":[33,84,183],"prescribed":[35],"target":[37,46,68],"using":[38],"only":[39],"matrix-vector":[40],"products":[41],"on":[42,71],"local":[43,154],"blocks.":[44],"Different":[45],"directions":[47],"can":[48],"be":[49],"recovered":[50],"independently":[51],"and":[52,60,118,127,144],"in":[53,80],"parallel.":[54],"establish":[56],"non-asymptotic":[57],"convergence":[58],"guarantees":[59],"provide":[61,114],"recursive":[63],"grid-search":[64],"procedure":[65],"for":[66,76,89],"selection.":[69],"Building":[70],"we":[73,102],"develop":[74],"BiFLICKER":[75,121,137,169],"community":[78],"detection":[79],"bipartite":[81,98],"networks,":[82],"together":[83],"sequential":[86],"projected":[87],"variant":[88],"nearby":[90],"or":[91],"repeated":[92],"directions.":[94],"Under":[95],"sparse":[97],"stochastic":[99],"block":[100],"model,":[101],"show":[103,135],"properly":[105],"rescaled":[106],"leading":[107],"values":[109],"from":[110],"reference":[112],"server":[113],"valid":[115],"global":[116],"targets,":[117],"Sequential":[120,168],"achieves":[122],"vanishing":[124],"misclustering":[125],"rate":[126],"exact":[128],"recovery":[129],"under":[130],"stronger":[131],"sparsity":[132],"conditions.":[133],"Simulations":[134],"remains":[138],"close":[139],"to":[140],"centralized":[141,177],"clustering":[143,178],"more":[146],"robust":[147],"than":[148],"existing":[149],"baselines":[152],"sample":[155],"sizes":[156],"are":[157],"small.":[158],"On":[159],"LastFM":[161],"user-artist":[162],"network":[163],"naturally":[164],"by":[166],"country,":[167],"attains":[170],"an":[171],"overall":[172],"ARI":[173],"of":[174],"0.94":[175],"against":[176],"while":[179],"revealing":[180],"cross-country":[181],"communities":[182],"distinct":[184],"listening":[185],"profiles.":[186]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-21T00:00:00"}