{"id":"https://openalex.org/W3007974554","doi":"https://doi.org/10.1109/bigdata47090.2019.9005983","title":"SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure","display_name":"SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007974554","doi":"https://doi.org/10.1109/bigdata47090.2019.9005983","mag":"3007974554"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9005983","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005983","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5037463236","display_name":"Guangxu Mei","orcid":"https://orcid.org/0000-0003-0279-4345"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guangxu Mei","raw_affiliation_strings":["Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100766486","display_name":"Ziyu Guo","orcid":"https://orcid.org/0000-0002-0310-3959"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyu Guo","raw_affiliation_strings":["Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002025125","display_name":"Shijun Liu","orcid":"https://orcid.org/0000-0002-4108-1391"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shijun Liu","raw_affiliation_strings":["Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100455171","display_name":"Pan Li","orcid":"https://orcid.org/0000-0001-6522-2446"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Pan","raw_affiliation_strings":["Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5037463236"],"corresponding_institution_ids":["https://openalex.org/I154099455"],"apc_list":null,"apc_paid":null,"fwci":4.185,"has_fulltext":false,"cited_by_count":49,"citation_normalized_percentile":{"value":0.95384946,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"2019","issue":null,"first_page":"2560","last_page":"2568"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9966999888420105,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9861999750137329,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.8102478981018066},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5946158766746521},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5366859436035156},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5283475518226624},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5172855854034424},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5033518671989441},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4910855293273926},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4823586344718933},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.47816377878189087},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4768422842025757},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4468914270401001},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.41099774837493896}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8102478981018066},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5946158766746521},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5366859436035156},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5283475518226624},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5172855854034424},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5033518671989441},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4910855293273926},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4823586344718933},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.47816377878189087},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4768422842025757},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4468914270401001},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.41099774837493896},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9005983","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005983","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"mag:3175014960","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002224045694859","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","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":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5099999904632568,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W2012609801","https://openalex.org/W2017099446","https://openalex.org/W2022322548","https://openalex.org/W2053637704","https://openalex.org/W2064675550","https://openalex.org/W2069351567","https://openalex.org/W2090891622","https://openalex.org/W2153579005","https://openalex.org/W2154851992","https://openalex.org/W2519887557","https://openalex.org/W2530417694","https://openalex.org/W2607500032","https://openalex.org/W2743104969","https://openalex.org/W2903890850","https://openalex.org/W2914853145","https://openalex.org/W2924719072","https://openalex.org/W2952575904","https://openalex.org/W2962767366","https://openalex.org/W2963555845","https://openalex.org/W2963858333","https://openalex.org/W2964015378","https://openalex.org/W2998704965","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W4285719527","https://openalex.org/W4294170691","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W6680532216","https://openalex.org/W6682691769","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6759403843","https://openalex.org/W6760886919","https://openalex.org/W6763057493","https://openalex.org/W6948004256"],"related_works":["https://openalex.org/W4287763734","https://openalex.org/W3035116611","https://openalex.org/W3094605108","https://openalex.org/W3044354590","https://openalex.org/W2923818335","https://openalex.org/W4212923699","https://openalex.org/W2893186803","https://openalex.org/W4310879833","https://openalex.org/W4226361842","https://openalex.org/W4284975088"],"abstract_inverted_index":{"Networks":[0],"are":[1,75],"general":[2],"tools":[3],"for":[4,20],"modeling":[5],"numerous":[6],"information":[7,27,30,106,127,149],"with":[8,25,164,206],"features":[9],"and":[10,31,107,167,178,194],"complex":[11],"relations.":[12],"Network":[13],"Embedding":[14],"aims":[15],"to":[16,46,84,145,154,170],"learn":[17],"low-dimension":[18],"representations":[19,50],"vertexes":[21],"in":[22,61,81,96,131],"the":[23,48,70,86,98,103,108,125,140,147,172],"network":[24,42,49,120,163],"rich":[26],"including":[28,102,189],"content":[29,78,105],"structural":[32,109],"information.":[33,110],"In":[34,111],"recent":[35],"years,":[36],"many":[37],"models":[38,205],"based":[39,76,174],"on":[40,77,175,185],"neural":[41,119,162],"have":[43,69],"been":[44],"proposed":[45,201],"map":[47],"into":[51],"embedding":[52],"space":[53],"whose":[54],"dimension":[55],"is":[56],"much":[57],"lower":[58],"than":[59],"that":[60,199],"original":[62,104,148],"space.":[63],"However,":[64],"most":[65],"of":[66,79,89,100,128,139,142],"existing":[67],"methods":[68],"following":[71],"limitations:":[72],"1)":[73],"they":[74,92],"nodes":[80,129,173],"network,":[82,192],"failing":[83],"measure":[85],"structure":[87,126],"similarity":[88],"nodes;":[90],"2)":[91],"cannot":[93],"do":[94],"well":[95],"protecting":[97],"privacy":[99],"users":[101],"this":[112],"paper,":[113],"we":[114],"propose":[115],"a":[116,207],"similarity-based":[117],"graph":[118,161],"model,":[121],"SGNN,":[122],"which":[123],"captures":[124],"precisely":[130],"node":[132,181],"classification":[133,182],"tasks.":[134],"It":[135],"also":[136],"takes":[137],"advantage":[138],"thought":[141],"federated":[143],"learning":[144],"hide":[146],"from":[150],"different":[151],"data":[152,187],"sources":[153],"protect":[155],"users'":[156],"privacy.":[157],"We":[158],"use":[159],"deep":[160],"convolutional":[165],"layers":[166,169],"dense":[168],"classify":[171],"their":[176],"structures":[177],"features.":[179],"The":[180],"experiment":[183],"results":[184],"public":[186],"sets":[188],"Aminer":[190],"coauthor":[191],"Brazil":[193],"Europe":[195],"flight":[196],"networks":[197],"indicate":[198],"our":[200],"model":[202],"outperforms":[203],"state-of-the-art":[204],"higher":[208],"accuracy.":[209]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
