{"id":"https://openalex.org/W2913305520","doi":"https://doi.org/10.1145/3308558.3313748","title":"Decoupled Smoothing on Graphs","display_name":"Decoupled Smoothing on Graphs","publication_year":2019,"publication_date":"2019-05-13","ids":{"openalex":"https://openalex.org/W2913305520","doi":"https://doi.org/10.1145/3308558.3313748","mag":"2913305520"},"language":"en","primary_location":{"id":"doi:10.1145/3308558.3313748","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313748","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The World Wide Web Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3308558.3313748","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101417223","display_name":"Alex Chin","orcid":"https://orcid.org/0000-0003-3280-177X"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Alex Chin","raw_affiliation_strings":["Stanford University, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012675734","display_name":"Yatong Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yatong Chen","raw_affiliation_strings":["Stanford University, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053740228","display_name":"Kristen M. Altenburger","orcid":"https://orcid.org/0000-0002-6575-1054"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kristen M. Altenburger","raw_affiliation_strings":["Stanford University, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072263832","display_name":"Johan Ugander","orcid":"https://orcid.org/0000-0001-5655-4086"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Johan Ugander","raw_affiliation_strings":["Stanford University, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University, USA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101417223"],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":1.5927,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.82926703,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"263","last_page":"272"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9922000169754028,"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"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9922000169754028,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.991599977016449,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9815000295639038,"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/smoothing","display_name":"Smoothing","score":0.7091387510299683},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6154680252075195},{"id":"https://openalex.org/keywords/homophily","display_name":"Homophily","score":0.5884406566619873},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.49422433972358704},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4595538377761841},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3317809998989105},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2715257406234741},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.13385862112045288}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.7091387510299683},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6154680252075195},{"id":"https://openalex.org/C2779812341","wikidata":"https://www.wikidata.org/wiki/Q5891525","display_name":"Homophily","level":2,"score":0.5884406566619873},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.49422433972358704},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4595538377761841},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3317809998989105},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2715257406234741},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.13385862112045288},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3308558.3313748","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313748","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The World Wide Web Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3308558.3313748","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313748","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The World Wide Web Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6600000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1491300635","https://openalex.org/W1529879082","https://openalex.org/W1533841329","https://openalex.org/W1871249542","https://openalex.org/W1989524890","https://openalex.org/W2004014822","https://openalex.org/W2009083767","https://openalex.org/W2011985563","https://openalex.org/W2016273060","https://openalex.org/W2053557769","https://openalex.org/W2056021151","https://openalex.org/W2074166361","https://openalex.org/W2082382938","https://openalex.org/W2086254934","https://openalex.org/W2102907934","https://openalex.org/W2114220616","https://openalex.org/W2126523478","https://openalex.org/W2130354913","https://openalex.org/W2136504847","https://openalex.org/W2137253512","https://openalex.org/W2139823104","https://openalex.org/W2150256170","https://openalex.org/W2154455818","https://openalex.org/W2167829771","https://openalex.org/W2288664612","https://openalex.org/W2315123323","https://openalex.org/W2333938294","https://openalex.org/W2472472130","https://openalex.org/W2560674852","https://openalex.org/W2790076745","https://openalex.org/W2802155208","https://openalex.org/W2802898185","https://openalex.org/W2914369697","https://openalex.org/W3100330855","https://openalex.org/W3124682511","https://openalex.org/W4248996458","https://openalex.org/W6680140577"],"related_works":["https://openalex.org/W3185373886","https://openalex.org/W3010567961","https://openalex.org/W2588006872","https://openalex.org/W4385338594","https://openalex.org/W4200127153","https://openalex.org/W3119171992","https://openalex.org/W2011190096","https://openalex.org/W3175275009","https://openalex.org/W2036947108","https://openalex.org/W4226363941"],"abstract_inverted_index":{"Graph":[0],"smoothing":[1,80,106,159],"methods":[2],"are":[3,92,124],"an":[4,116,161],"extremely":[5],"popular":[6],"family":[7],"of":[8,15,88,110,120,145,165],"approaches":[9],"for":[10,54,69],"semi-supervised":[11],"learning.":[12],"The":[13],"choice":[14,41],"graph":[16,60,68,74,79,105,155,164],"used":[17],"to":[18,83,94,104,126],"represent":[19],"relationships":[20],"in":[21,45,115,134,141],"these":[22],"learning":[23],"problems":[24],"is":[25,42,81],"often":[26,63],"a":[27,101,142],"more":[28],"important":[29],"decision":[30],"than":[31],"the":[32,46,57,66,85,146],"particular":[33],"algorithm":[34],"or":[35,167],"loss":[36],"function":[37],"used,":[38],"yet":[39],"this":[40,49],"less":[43],"well-studied":[44],"literature.":[47],"In":[48],"work,":[50],"we":[51],"demonstrate":[52],"that":[53,107,153],"social":[55,86,118],"networks,":[56],"basic":[58],"friendship":[59],"itself":[61],"may":[62],"not":[64],"be":[65],"appropriate":[67,162],"predicting":[70],"node":[71],"attributes":[72],"using":[73],"smoothing.":[75],"More":[76],"specifically,":[77],"standard":[78],"designed":[82],"harness":[84],"phenomenon":[87,119],"homophily":[89],"whereby":[90,122],"individuals":[91,123],"similar":[93,125],"\u201cthe":[95,127],"company":[96,128],"they":[97],"keep.\u201d":[98],"We":[99],"present":[100],"decoupled":[102],"approach":[103],"decouples":[108],"notions":[109],"\u201cidentity\u201d":[111],"and":[112],"\u201cpreference,\u201d":[113],"resulting":[114],"alternative":[117],"monophily":[121],"they're":[129],"kept":[130],"in,\u201d":[131],"as":[132,158],"observed":[133],"recent":[135],"empirical":[136],"work.":[137],"Our":[138],"model":[139],"results":[140],"rigorous":[143],"extension":[144],"Gaussian":[147],"Markov":[148],"Random":[149],"Field":[150],"(GMRF)":[151],"models":[152],"underlie":[154],"smoothing,":[156],"interpretable":[157],"on":[160],"auxiliary":[163],"weighted":[166],"unweighted":[168],"two-hop":[169],"relationships.":[170]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
