{"id":"https://openalex.org/W1975488756","doi":"https://doi.org/10.1145/2484028.2484086","title":"Incorporating popularity in topic models for social network analysis","display_name":"Incorporating popularity in topic models for social network analysis","publication_year":2013,"publication_date":"2013-07-28","ids":{"openalex":"https://openalex.org/W1975488756","doi":"https://doi.org/10.1145/2484028.2484086","mag":"1975488756"},"language":"en","primary_location":{"id":"doi:10.1145/2484028.2484086","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2484028.2484086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","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/A5027815777","display_name":"Youngchul Cha","orcid":null},"institutions":[{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"funder","lineage":["https://openalex.org/I2799798094"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Youngchul Cha","raw_affiliation_strings":["UCLA, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"UCLA, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I2799798094"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006355964","display_name":"Bin Bi","orcid":null},"institutions":[{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"funder","lineage":["https://openalex.org/I2799798094"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bin Bi","raw_affiliation_strings":["UCLA, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"UCLA, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I2799798094"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061512142","display_name":"Chu-Cheng Hsieh","orcid":null},"institutions":[{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"funder","lineage":["https://openalex.org/I2799798094"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chu-Cheng Hsieh","raw_affiliation_strings":["UCLA, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"UCLA, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I2799798094"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073859348","display_name":"Junghoo Cho","orcid":null},"institutions":[{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"funder","lineage":["https://openalex.org/I2799798094"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junghoo Cho","raw_affiliation_strings":["UCLA, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"UCLA, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I2799798094"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5027815777"],"corresponding_institution_ids":["https://openalex.org/I2799798094"],"apc_list":null,"apc_paid":null,"fwci":4.7355,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.9500676,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"223","last_page":"232"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9958000183105469,"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":0.9948999881744385,"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/popularity","display_name":"Popularity","score":0.8991700410842896},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7197998762130737},{"id":"https://openalex.org/keywords/social-network-analysis","display_name":"Social network analysis","score":0.5874751806259155},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4375481903553009},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.32619452476501465},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.26365774869918823},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.11663660407066345}],"concepts":[{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.8991700410842896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7197998762130737},{"id":"https://openalex.org/C114713312","wikidata":"https://www.wikidata.org/wiki/Q7551269","display_name":"Social network analysis","level":3,"score":0.5874751806259155},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4375481903553009},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.32619452476501465},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.26365774869918823},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.11663660407066345},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2484028.2484086","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2484028.2484086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.309.3963","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.309.3963","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://rose.cs.ucla.edu/~cho/papers/SIGIR13.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1732828232","https://openalex.org/W1738091461","https://openalex.org/W1786797669","https://openalex.org/W1880262756","https://openalex.org/W1971817345","https://openalex.org/W1980672078","https://openalex.org/W1982167371","https://openalex.org/W1996174542","https://openalex.org/W1997136459","https://openalex.org/W2001747839","https://openalex.org/W2036120890","https://openalex.org/W2046974451","https://openalex.org/W2048330531","https://openalex.org/W2063397738","https://openalex.org/W2064153289","https://openalex.org/W2066636486","https://openalex.org/W2072515820","https://openalex.org/W2090876554","https://openalex.org/W2100348674","https://openalex.org/W2101409192","https://openalex.org/W2103587173","https://openalex.org/W2107107106","https://openalex.org/W2107743791","https://openalex.org/W2108346334","https://openalex.org/W2109154616","https://openalex.org/W2111976284","https://openalex.org/W2117172769","https://openalex.org/W2123549998","https://openalex.org/W2124187902","https://openalex.org/W2131689821","https://openalex.org/W2138228978","https://openalex.org/W2142534468","https://openalex.org/W2145677303","https://openalex.org/W2147152072","https://openalex.org/W2150461699","https://openalex.org/W2165599843","https://openalex.org/W2165636119","https://openalex.org/W2170246630","https://openalex.org/W2185870924","https://openalex.org/W2252926514","https://openalex.org/W2309755354","https://openalex.org/W2334889010","https://openalex.org/W2596701210","https://openalex.org/W2963683999","https://openalex.org/W3016356276","https://openalex.org/W4233135949","https://openalex.org/W4239545644"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2368605798","https://openalex.org/W2518037665","https://openalex.org/W2348524959","https://openalex.org/W2477036161","https://openalex.org/W2368049389","https://openalex.org/W2093123876","https://openalex.org/W4388192780","https://openalex.org/W4320063274","https://openalex.org/W3151710375"],"abstract_inverted_index":{"Topic":[0],"models":[1,33,160,168],"are":[2,89,111],"used":[3],"to":[4,94,178,184],"group":[5],"words":[6,21,39,62],"in":[7,24,42,55,83,113,128,223],"a":[8,12,19,25,52,56,78,84,137,140,162],"text":[9,57],"dataset":[10,58,87],"into":[11],"set":[13],"of":[14,125,139,158,202],"relevant":[15],"topics.":[16],"Unfortunately,":[17],"when":[18],"few":[20],"frequently":[22],"appear":[23,41],"dataset,":[26],"the":[27,60,64,123,151,156,179,185,190,200,203],"topic":[28,32,45,79,129,145,209],"groups":[29],"identified":[30],"by":[31,189],"become":[34],"noisy":[35],"because":[36,59,108],"these":[37],"frequent":[38,61],"repeatedly":[40],"\u201cirrelevant":[43],"\u201d":[44],"groups.":[46],"This":[47],"noise":[48],"has":[49],"not":[50,68,220],"been":[51,74],"serious":[53],"problem":[54,187],"(e.g.,":[63,97],"and":[65,72,100,103,143],"is)":[66],"do":[67],"have":[69,73],"much":[70,222],"meaning":[71],"simply":[75,106],"removed":[76,107],"before":[77],"model":[80,122,146,210],"analysis.":[81],"However,":[82],"social":[85],"network":[86],"we":[88,120,134,197],"interested":[90,112],"in,":[91],"they":[92],"correspond":[93],"popular":[95],"persons":[96],"Barack":[98],"Obama":[99],"Justin":[101],"Bieber)":[102],"cannot":[104],"be":[105],"most":[109],"people":[110],"them.":[114],"To":[115],"solve":[116],"this":[117,132],"\u201cpopularity":[118,141],"problem\u201d,":[119],"explicitly":[121],"popularity":[124,152,186],"nodes":[126,191],"(words)":[127],"models.":[130],"For":[131],"purpose,":[133],"first":[135],"introduce":[136],"notion":[138],"component\u201d":[142],"propose":[144],"extensions":[147],"that":[148,214],"effectively":[149],"accommodate":[150],"component.":[153],"We":[154,212],"evaluate":[155],"effectiveness":[157],"our":[159],"with":[161,192,207],"real-world":[163],"Twitter":[164],"dataset.":[165],"Our":[166],"proposed":[167],"achieve":[169],"significantly":[170],"lower":[171,225],"perplexity":[172],"(i.e.,":[173],"better":[174],"prediction":[175],"power)":[176],"compared":[177],"state-of-theart":[180],"baselines.":[181],"In":[182],"addition":[183],"caused":[188],"high":[193],"incoming":[194],"edge":[195,205,217],"degree,":[196],"also":[198],"investigate":[199],"effect":[201],"outgoing":[204,216],"degree":[206,218],"another":[208],"extensions.":[211],"show":[213],"considering":[215],"does":[219],"help":[221],"achieving":[224],"perplexity.":[226]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":9},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":8}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}