{"id":"https://openalex.org/W2028513945","doi":"https://doi.org/10.1145/2339530.2339612","title":"Low rank modeling of signed networks","display_name":"Low rank modeling of signed networks","publication_year":2012,"publication_date":"2012-08-12","ids":{"openalex":"https://openalex.org/W2028513945","doi":"https://doi.org/10.1145/2339530.2339612","mag":"2028513945"},"language":"en","primary_location":{"id":"doi:10.1145/2339530.2339612","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339612","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5010841999","display_name":"Cho\u2010Jui Hsieh","orcid":"https://orcid.org/0000-0002-3520-9627"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cho-Jui Hsieh","raw_affiliation_strings":["University of Texas at Austin, Austin, TX, USA","University of Texas at Austin, Austin, TX USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]},{"raw_affiliation_string":"University of Texas at Austin, Austin, TX USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057095691","display_name":"Kai-Yang Chiang","orcid":null},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kai-Yang Chiang","raw_affiliation_strings":["University of Texas at Austin, Austin, TX, USA","University of Texas at Austin, Austin, TX USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]},{"raw_affiliation_string":"University of Texas at Austin, Austin, TX USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063459703","display_name":"Inderjit S. Dhillon","orcid":"https://orcid.org/0000-0002-2759-1416"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Inderjit S. Dhillon","raw_affiliation_strings":["University of Texas at Austin, Austin, TX, USA","University of Texas at Austin, Austin, TX USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]},{"raw_affiliation_string":"University of Texas at Austin, Austin, TX USA","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":17.1761,"has_fulltext":false,"cited_by_count":153,"citation_normalized_percentile":{"value":0.99591281,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"507","last_page":"515"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9987999796867371,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.996399998664856,"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/distrust","display_name":"Distrust","score":0.7387579679489136},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7311491370201111},{"id":"https://openalex.org/keywords/signed-graph","display_name":"Signed graph","score":0.6889328956604004},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6577110290527344},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5640749931335449},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.498126745223999},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.48487839102745056},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.46340441703796387},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4583527147769928},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33686962723731995},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.2857949733734131},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23201638460159302},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.08372610807418823}],"concepts":[{"id":"https://openalex.org/C2778321746","wikidata":"https://www.wikidata.org/wiki/Q621922","display_name":"Distrust","level":2,"score":0.7387579679489136},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7311491370201111},{"id":"https://openalex.org/C2779773260","wikidata":"https://www.wikidata.org/wiki/Q11246292","display_name":"Signed graph","level":3,"score":0.6889328956604004},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6577110290527344},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5640749931335449},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.498126745223999},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.48487839102745056},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.46340441703796387},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4583527147769928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33686962723731995},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.2857949733734131},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23201638460159302},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.08372610807418823},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2339530.2339612","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339612","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.258.7177","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.258.7177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.utexas.edu/users/inderjit/public_papers/signlowrank_kdd12.pdf","raw_type":"text"}],"best_oa_location":null,"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/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306078","display_name":"U.S. Department of Defense","ror":"https://ror.org/0447fe631"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W108936587","https://openalex.org/W1573301526","https://openalex.org/W1595449516","https://openalex.org/W1964537599","https://openalex.org/W1973836644","https://openalex.org/W1980769375","https://openalex.org/W2054141820","https://openalex.org/W2073415627","https://openalex.org/W2097216034","https://openalex.org/W2103972604","https://openalex.org/W2114051435","https://openalex.org/W2118550318","https://openalex.org/W2128269848","https://openalex.org/W2134332047","https://openalex.org/W2135957668","https://openalex.org/W2142562746","https://openalex.org/W2144780381","https://openalex.org/W2156894402","https://openalex.org/W2165874743","https://openalex.org/W2611328865","https://openalex.org/W4298299405","https://openalex.org/W6643806823","https://openalex.org/W6681259990","https://openalex.org/W6684578312"],"related_works":["https://openalex.org/W2972762740","https://openalex.org/W2987940850","https://openalex.org/W3089774482","https://openalex.org/W4287824815","https://openalex.org/W3012790960","https://openalex.org/W2241576602","https://openalex.org/W3042351536","https://openalex.org/W3086597576","https://openalex.org/W4225616444","https://openalex.org/W3088729862"],"abstract_inverted_index":{"Trust":[0],"networks,":[1,88,197,239],"where":[2,21],"people":[3],"leave":[4],"trust":[5,30,67],"and":[6,98,216,291],"distrust":[7,69],"feedback,":[8],"are":[9],"becoming":[10],"increasingly":[11,49],"common.":[12],"These":[13],"networks":[14,45,118],"may":[15],"be":[16,138,206,231],"regarded":[17],"as":[18,140,178,241,254,256,271,273,306,324],"signed":[19,44,75,87,117,196,238,245],"graphs,":[20],"a":[22,32,72,122,131,141,169,179,209,312],"positive":[23],"edge":[24,34],"weight":[25,35],"captures":[26,36],"the":[27,37,81,101,110,127,133,166,227,279,302,330],"degree":[28,38],"of":[29,39,42,59,83,112,168,268],"while":[31,191],"negative":[33],"distrust.":[40],"Analysis":[41],"such":[43,130,240],"has":[46],"become":[47],"an":[48,276],"important":[50,54],"research":[51],"topic.":[52],"One":[53],"analysis":[55,204,235],"task":[56,305],"is":[57],"that":[58,109,148,202,226,260],"sign":[60,134,183,269,303],"inference,":[61],"i.e.,":[62],"infer":[63],"unknown":[64],"(or":[65],"future)":[66],"or":[68],"relationships":[70],"given":[71],"partially":[73],"observed":[74],"network.":[76,102,128],"Most":[77],"state-of-the-art":[78,159,313],"approaches":[79],"consider":[80],"notion":[82,111],"structural":[84,114],"balance":[85,115],"in":[86,100,116,220,321],"building":[89],"inference":[90,135,184,270,304],"algorithms":[91],"based":[92],"on":[93,208,237,252,278,301],"information":[94],"about":[95],"links,":[96],"triads,":[97],"cycles":[99],"In":[103],"this":[104,186,203],"paper,":[105],"we":[106,149,200],"first":[107],"show":[108,147,201,225,259],"weak":[113],"naturally":[119],"leads":[120],"to":[121,194,284,308,326],"global":[123],"low-rank":[124,142,170,228],"model":[125,229,264],"for":[126,182,198,233,329],"Under":[129],"model,":[132],"problem":[136],"can":[137,150,205,230],"formulated":[139],"matrix":[143,160,171,295],"completion":[144,161],"problem.":[145],"We":[146,163,223],"perfectly":[151],"recover":[152],"missing":[153],"relationships,":[154],"under":[155],"certain":[156],"conditions,":[157],"using":[158,311],"algorithms.":[162],"also":[164],"propose":[165],"use":[167],"factorization":[172,296],"approach":[173,187,297],"with":[174,212,248,288],"generalized":[175],"loss":[176],"functions":[177],"practical":[180],"method":[181,315,319],"-":[185,316],"yields":[188,298],"high":[189],"accuracy":[190,267,300,310],"being":[192],"scalable":[193],"large":[195],"instance,":[199],"performed":[207],"synthetic":[210,253],"graph":[211,246],"1.1":[213],"million":[214,218],"nodes":[215,290],"120":[217],"edges":[219],"10":[221],"minutes.":[222],"further":[224],"used":[232],"other":[234],"tasks":[236],"user":[242],"segmentation":[243],"through":[244],"clustering,":[247],"theoretical":[249],"guarantees.":[250],"Experiments":[251],"well":[255,272],"real":[257,281],"data":[258,287],"our":[261,294,318],"low":[262],"rank":[263],"substantially":[265],"improves":[266],"clustering.":[274],"As":[275],"example,":[277],"largest":[280],"dataset":[282],"available":[283],"us":[285],"(Epinions":[286],"130K":[289],"840K":[292],"edges),":[293],"94.6%":[299],"compared":[307,325],"90.8%":[309],"cycle-based":[314,331],"moreover,":[317],"runs":[320],"40":[322],"seconds":[323,328],"10,000":[327],"method.":[332]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":21},{"year":2019,"cited_by_count":23},{"year":2018,"cited_by_count":16},{"year":2017,"cited_by_count":21},{"year":2016,"cited_by_count":8},{"year":2015,"cited_by_count":9},{"year":2014,"cited_by_count":11},{"year":2013,"cited_by_count":8},{"year":2012,"cited_by_count":3}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}
