{"id":"https://openalex.org/W4224951377","doi":"https://doi.org/10.24963/ijcai.2022/294","title":"Gromov-Wasserstein Discrepancy with Local Differential Privacy for Distributed Structural Graphs","display_name":"Gromov-Wasserstein Discrepancy with Local Differential Privacy for Distributed Structural Graphs","publication_year":2022,"publication_date":"2022-07-01","ids":{"openalex":"https://openalex.org/W4224951377","doi":"https://doi.org/10.24963/ijcai.2022/294"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2022/294","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/294","pdf_url":"https://www.ijcai.org/proceedings/2022/0294.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","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 Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.ijcai.org/proceedings/2022/0294.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101746526","display_name":"Hongwei Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongwei Jin","raw_affiliation_strings":["University of Illinois at Chicago"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100451602","display_name":"Xun Chen","orcid":"https://orcid.org/0000-0002-4922-8116"},"institutions":[{"id":"https://openalex.org/I4210133173","display_name":"Research!America (United States)","ror":"https://ror.org/044pgyv50","country_code":"US","type":"company","lineage":["https://openalex.org/I4210133173"]},{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xun Chen","raw_affiliation_strings":["Samsung Research America","Samsung Research America,"],"affiliations":[{"raw_affiliation_string":"Samsung Research America","institution_ids":["https://openalex.org/I4210101778","https://openalex.org/I4210133173"]},{"raw_affiliation_string":"Samsung Research America,","institution_ids":["https://openalex.org/I4210101778","https://openalex.org/I4210133173"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100451602"],"corresponding_institution_ids":["https://openalex.org/I4210101778","https://openalex.org/I4210133173"],"apc_list":null,"apc_paid":null,"fwci":0.2087,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.38108108,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2115","last_page":"2121"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9991000294685364,"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":0.9991000294685364,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9968000054359436,"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/T12536","display_name":"Topological and Geometric Data Analysis","score":0.9693999886512756,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/differential-privacy","display_name":"Differential privacy","score":0.8510005474090576},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7387200593948364},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5816421508789062},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5233854055404663},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5218040943145752},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.4471016824245453},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4286089241504669},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.42511898279190063},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4239575266838074},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42200398445129395},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4105263948440552},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31880319118499756},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.15162155032157898}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.8510005474090576},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7387200593948364},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5816421508789062},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5233854055404663},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5218040943145752},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.4471016824245453},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4286089241504669},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.42511898279190063},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4239575266838074},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42200398445129395},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4105263948440552},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31880319118499756},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.15162155032157898},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2022/294","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/294","pdf_url":"https://www.ijcai.org/proceedings/2022/0294.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","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 Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2022/294","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/294","pdf_url":"https://www.ijcai.org/proceedings/2022/0294.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","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 Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5600000023841858}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332195","display_name":"Samsung","ror":"https://ror.org/04w3jy968"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4224951377.pdf","grobid_xml":"https://content.openalex.org/works/W4224951377.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W2027842533","https://openalex.org/W2060393849","https://openalex.org/W2099223028","https://openalex.org/W2142498761","https://openalex.org/W2147286743","https://openalex.org/W2417433140","https://openalex.org/W2592651140","https://openalex.org/W2909805545","https://openalex.org/W2911397097","https://openalex.org/W2914721378","https://openalex.org/W2955783194","https://openalex.org/W2962711740","https://openalex.org/W2963312446","https://openalex.org/W2966149470","https://openalex.org/W2997619488","https://openalex.org/W3022227446","https://openalex.org/W3037879915","https://openalex.org/W3042313988","https://openalex.org/W3044211235","https://openalex.org/W3121235808","https://openalex.org/W3154667344","https://openalex.org/W3155768653","https://openalex.org/W3161879162","https://openalex.org/W3214714397","https://openalex.org/W4231645939","https://openalex.org/W4287667661","https://openalex.org/W4288350653","https://openalex.org/W4294558607","https://openalex.org/W4296300780","https://openalex.org/W4300427714","https://openalex.org/W4318619660","https://openalex.org/W4318822841"],"related_works":["https://openalex.org/W3038283795","https://openalex.org/W2604501336","https://openalex.org/W3041656022","https://openalex.org/W4280591108","https://openalex.org/W4387042211","https://openalex.org/W3175367179","https://openalex.org/W3101329503","https://openalex.org/W4289655333","https://openalex.org/W4221146017","https://openalex.org/W2981981504"],"abstract_inverted_index":{"Learning":[0],"the":[1,7,12,16,44,65,88,132,167,170],"similarity":[2],"between":[3],"structured":[4,48],"data,":[5],"especially":[6],"graphs,":[8],"is":[9,68],"one":[10],"of":[11,91],"essential":[13],"problems.":[14],"Besides":[15],"approach":[17],"like":[18],"graph":[19,97,140],"kernels,":[20],"Gromov-Wasserstein":[21],"(GW)":[22],"distance":[23,173],"recently":[24],"draws":[25],"a":[26,83,101,145],"big":[27],"attention":[28],"due":[29],"to":[30,33,70,86,116],"its":[31],"flexibility":[32],"capture":[34],"both":[35],"topological":[36],"and":[37,57,104,155,161,175],"feature":[38],"characteristics,":[39],"as":[40,42],"well":[41],"handling":[43],"permutation":[45],"invariance.":[46],"However,":[47],"data":[49,55,67],"are":[50],"widely":[51],"distributed":[52],"for":[53],"different":[54,75],"mining":[56],"machine":[58],"learning":[59],"applications.":[60],"With":[61],"privacy":[62,110,126,138],"concerns,":[63],"accessing":[64],"decentralized":[66],"limited":[69],"either":[71],"individual":[72],"clients":[73],"or":[74],"silos.":[76],"To":[77],"tackle":[78],"these":[79],"issues,":[80],"we":[81,165],"propose":[82],"privacy-preserving":[84],"framework":[85,134],"analyze":[87],"GW":[89,150,172],"discrepancy":[90],"node":[92],"embedding":[93],"learned":[94],"locally":[95],"from":[96],"neural":[98],"networks":[99],"in":[100,139,153,159],"federated":[102],"flavor,":[103],"then":[105],"explicitly":[106],"place":[107],"local":[108],"differential":[109],"(LDP)":[111],"based":[112],"on":[113],"Multi-bit":[114],"Encoder":[115],"protect":[117],"sensitive":[118],"information.":[119],"Our":[120],"experiments":[121],"show":[122],"that,":[123],"with":[124],"strong":[125],"protection":[127],"guaranteed":[128],"by":[129],"\u03b5-LDP":[130],"algorithm,":[131],"proposed":[133],"not":[135],"only":[136],"preserves":[137],"learning,":[141],"but":[142],"also":[143],"presents":[144],"noised":[146],"structural":[147],"metric":[148],"under":[149],"distance,":[151],"resulting":[152],"comparable":[154],"even":[156],"better":[157],"performance":[158],"classification":[160],"clustering":[162],"tasks.":[163],"Moreover,":[164],"reason":[166],"rationale":[168],"behind":[169],"LDP-based":[171],"analytically":[174],"empirically.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}