{"id":"https://openalex.org/W3169236624","doi":"https://doi.org/10.1145/3447548.3467355","title":"Signed Graph Neural Network with Latent Groups","display_name":"Signed Graph Neural Network with Latent Groups","publication_year":2021,"publication_date":"2021-08-13","ids":{"openalex":"https://openalex.org/W3169236624","doi":"https://doi.org/10.1145/3447548.3467355","mag":"3169236624"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467355","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467355","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining","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/A5005957133","display_name":"Haoxin Liu","orcid":"https://orcid.org/0000-0002-9237-0708"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haoxin Liu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100442619","display_name":"Ziwei Zhang","orcid":"https://orcid.org/0000-0003-2451-843X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziwei Zhang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009228005","display_name":"Peng Cui","orcid":"https://orcid.org/0000-0003-2957-8511"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Cui","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100613922","display_name":"Yafeng Zhang","orcid":"https://orcid.org/0000-0002-0876-0499"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yafeng Zhang","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101625223","display_name":"Qiang Cui","orcid":"https://orcid.org/0000-0001-9885-2452"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Cui","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004072676","display_name":"Jiashuo Liu","orcid":"https://orcid.org/0000-0002-9159-1752"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiashuo Liu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100339293","display_name":"Wenwu Zhu","orcid":"https://orcid.org/0000-0003-2236-9290"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenwu Zhu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5005957133"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":2.5196,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":{"value":0.91028267,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1066","last_page":"1075"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"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":1.0,"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":0.9977999925613403,"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/T10028","display_name":"Topic Modeling","score":0.9574000239372253,"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":0.6354191303253174},{"id":"https://openalex.org/keywords/signed-graph","display_name":"Signed graph","score":0.6196501851081848},{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.5937424898147583},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5391993522644043},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4467396140098572},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.4435505270957947},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3924816846847534},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2685997486114502},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.10762569308280945},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.09613421559333801}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6354191303253174},{"id":"https://openalex.org/C2779773260","wikidata":"https://www.wikidata.org/wiki/Q11246292","display_name":"Signed graph","level":3,"score":0.6196501851081848},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.5937424898147583},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5391993522644043},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4467396140098572},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.4435505270957947},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3924816846847534},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2685997486114502},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.10762569308280945},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.09613421559333801}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447548.3467355","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467355","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.4000000059604645}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W1980769375","https://openalex.org/W1986936900","https://openalex.org/W2004026774","https://openalex.org/W2060881157","https://openalex.org/W2066215526","https://openalex.org/W2142517301","https://openalex.org/W2147468287","https://openalex.org/W2154851992","https://openalex.org/W2294720570","https://openalex.org/W2393319904","https://openalex.org/W2415243320","https://openalex.org/W2519887557","https://openalex.org/W2576802060","https://openalex.org/W2585835859","https://openalex.org/W2606780347","https://openalex.org/W2622849676","https://openalex.org/W2767500585","https://openalex.org/W2788045146","https://openalex.org/W2788284887","https://openalex.org/W2811124557","https://openalex.org/W2887092413","https://openalex.org/W2922291765","https://openalex.org/W2939208918","https://openalex.org/W2951533109","https://openalex.org/W2953060237","https://openalex.org/W2962711740","https://openalex.org/W2962756421","https://openalex.org/W2963239711","https://openalex.org/W2964051675","https://openalex.org/W2973140148","https://openalex.org/W2997638284","https://openalex.org/W3011667710","https://openalex.org/W3034402203","https://openalex.org/W3099069123","https://openalex.org/W3099565317","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W3114961718","https://openalex.org/W3119227986","https://openalex.org/W4206046795"],"related_works":["https://openalex.org/W4364386131","https://openalex.org/W2391817034","https://openalex.org/W1232590083","https://openalex.org/W4297684948","https://openalex.org/W2029145332","https://openalex.org/W4295093499","https://openalex.org/W2000749863","https://openalex.org/W3169236624","https://openalex.org/W1985498722","https://openalex.org/W2121340930"],"abstract_inverted_index":{"Signed":[0,85],"graph":[1,28,93],"representation":[2,29,94],"learning":[3,30,95],"is":[4,52,209,246],"an":[5],"effective":[6],"approach":[7],"to":[8,33,55,70,149,161,193],"analyze":[9],"the":[10,18,45,72,97,110,113,117,137,155,158,163,204,218,237],"complex":[11],"patterns":[12],"in":[13,167,248],"real-world":[14,228],"signed":[15,27,76,92,229],"graphs":[16,230],"with":[17],"co-existence":[19],"of":[20,74,109,206,220],"positive":[21,179],"and":[22,112,135,143,181,188,203,227,233,239],"negative":[23,182],"links.":[24],"Most":[25],"previous":[26],"methods":[31],"resort":[32],"balance":[34,50,98],"theory,":[35],"a":[36,56,103,122,145,190],"classic":[37],"social":[38],"theory":[39,51,99],"that":[40,59,107,126,136],"originated":[41],"from":[42],"psychology":[43],"as":[44,184],"core":[46],"assumption.":[47,100,156],"However,":[48],"since":[49],"shown":[53],"equivalent":[54],"simple":[57],"assumption":[58,125],"nodes":[60,127],"can":[61,128,139,199],"be":[62,129],"divided":[63,130],"into":[64,131,211],"two":[65,185,207],"conflicting":[66],"groups,":[67],"it":[68],"fails":[69],"model":[71,90,164,223],"structure":[73],"real":[75],"graphs.":[77],"To":[78],"solve":[79],"this":[80],"problem,":[81],"we":[82,120,171],"propose":[83,144],"Group":[84],"Graph":[86],"Neural":[87],"Network":[88],"(GS-GNN)":[89],"for":[91],"beyond":[96],"GS-GNN":[101,222],"has":[102],"dual":[104],"GNN":[105,148,192],"architecture":[106],"consists":[108],"global":[111,118],"local":[114,159],"module.":[115],"In":[116,157],"module,":[119,160],"adopt":[121,189],"more":[123],"generalized":[124],"multiple":[132],"latent":[133],"groups":[134,138],"have":[140],"arbitrary":[141],"relations":[142],"novel":[146],"prototype-based":[147],"learn":[150,194],"node":[151,195],"representations":[152],"based":[153],"on":[154,224],"give":[162],"enough":[165],"flexibility":[166],"modeling":[168],"other":[169],"factors,":[170],"do":[172],"not":[173],"make":[174],"any":[175],"prior":[176],"assumptions,":[177],"treat":[178],"links":[180,183],"independent":[186],"relations,":[187],"relational":[191],"representations.":[196],"Both":[197],"modules":[198,208],"complement":[200],"each":[201],"other,":[202],"concatenation":[205],"fed":[210],"downstream":[212],"tasks.":[213],"Extensive":[214],"experimental":[215],"results":[216],"demonstrate":[217],"effectiveness":[219],"our":[221],"both":[225],"synthetic":[226],"by":[231],"greatly":[232],"consistently":[234],"outperforming":[235],"all":[236],"baselines":[238],"achieving":[240],"new":[241],"state-of-the-art":[242],"results.":[243],"Our":[244],"implementation":[245],"available":[247],"PyTorch.":[249]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":5}],"updated_date":"2026-04-28T14:05:53.105641","created_date":"2025-10-10T00:00:00"}
