{"id":"https://openalex.org/W2536420199","doi":"https://doi.org/10.1109/dsaa.2014.7058095","title":"Inferring potential users in mobile social networks","display_name":"Inferring potential users in mobile social networks","publication_year":2014,"publication_date":"2014-10-01","ids":{"openalex":"https://openalex.org/W2536420199","doi":"https://doi.org/10.1109/dsaa.2014.7058095","mag":"2536420199"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa.2014.7058095","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2014.7058095","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Conference on Data Science and Advanced Analytics (DSAA)","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/A5070582383","display_name":"Tsung-Hao Hsu","orcid":null},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Tsung-Hao Hsu","raw_affiliation_strings":["National Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101770112","display_name":"Chien\u2010Cheng Chen","orcid":"https://orcid.org/0000-0002-1684-4297"},"institutions":[{"id":"https://openalex.org/I92172085","display_name":"Chunghwa Telecom (Taiwan)","ror":"https://ror.org/04f786589","country_code":"TW","type":"company","lineage":["https://openalex.org/I92172085"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chien-Cheng Chen","raw_affiliation_strings":["Chunghwa Telecom Lab., Taoyuan, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chunghwa Telecom Lab., Taoyuan, Taiwan","institution_ids":["https://openalex.org/I92172085"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024176415","display_name":"Meng-Fen Chiang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210143126","display_name":"Acer (Taiwan)","ror":"https://ror.org/03xajsx66","country_code":"TW","type":"company","lineage":["https://openalex.org/I4210143126"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Meng-Fen Chiang","raw_affiliation_strings":["Yahoo Inc., Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Inc., Taipei, Taiwan","institution_ids":["https://openalex.org/I4210143126"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039657695","display_name":"Kuo-Wei Hsu","orcid":"https://orcid.org/0000-0002-3496-5439"},"institutions":[{"id":"https://openalex.org/I87354575","display_name":"National Chengchi University","ror":"https://ror.org/03rqk8h36","country_code":"TW","type":"education","lineage":["https://openalex.org/I87354575"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Kuo-Wei Hsu","raw_affiliation_strings":["National Chengchi University, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Chengchi University, Taipei, Taiwan","institution_ids":["https://openalex.org/I87354575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102958591","display_name":"Wen-Chih Peng","orcid":"https://orcid.org/0000-0001-8964-0933"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Wen-Chih Peng","raw_affiliation_strings":["National Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8785,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83135392,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"14","issue":null,"first_page":"347","last_page":"353"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9944999814033508,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9840999841690063,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.849795937538147},{"id":"https://openalex.org/keywords/competitor-analysis","display_name":"Competitor analysis","score":0.71207594871521},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6341358423233032},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5332064032554626},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5329678058624268},{"id":"https://openalex.org/keywords/social-network","display_name":"Social network (sociolinguistics)","score":0.46403977274894714},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46048909425735474},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.44494959712028503},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4220832884311676},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3924916982650757},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3229495882987976},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.3004213869571686},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.29227542877197266},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1294099986553192}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.849795937538147},{"id":"https://openalex.org/C127576917","wikidata":"https://www.wikidata.org/wiki/Q624630","display_name":"Competitor analysis","level":2,"score":0.71207594871521},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6341358423233032},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5332064032554626},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5329678058624268},{"id":"https://openalex.org/C4727928","wikidata":"https://www.wikidata.org/wiki/Q17164759","display_name":"Social network (sociolinguistics)","level":3,"score":0.46403977274894714},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46048909425735474},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.44494959712028503},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4220832884311676},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3924916982650757},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3229495882987976},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3004213869571686},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.29227542877197266},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1294099986553192},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa.2014.7058095","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2014.7058095","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W27394026","https://openalex.org/W47923795","https://openalex.org/W1673310716","https://openalex.org/W1680392829","https://openalex.org/W1966021193","https://openalex.org/W2003707464","https://openalex.org/W2017102965","https://openalex.org/W2026369636","https://openalex.org/W2042123098","https://openalex.org/W2047940964","https://openalex.org/W2056609785","https://openalex.org/W2095054612","https://openalex.org/W2095293504","https://openalex.org/W2102717275","https://openalex.org/W2112076978","https://openalex.org/W2117764127","https://openalex.org/W2142827986","https://openalex.org/W2153635508","https://openalex.org/W2293994416","https://openalex.org/W2435251607","https://openalex.org/W2911964244","https://openalex.org/W6637131181","https://openalex.org/W6637386731","https://openalex.org/W6676769703","https://openalex.org/W6717827561"],"related_works":["https://openalex.org/W2358804928","https://openalex.org/W4225710828","https://openalex.org/W1998528887","https://openalex.org/W2965538880","https://openalex.org/W2143282039","https://openalex.org/W2528370785","https://openalex.org/W4200335562","https://openalex.org/W2861933770","https://openalex.org/W2361145238","https://openalex.org/W2371267447"],"abstract_inverted_index":{"In":[0],"mobile":[1],"social":[2],"networks,":[3],"users":[4,51,68,125],"can":[5,195],"communicate":[6],"with":[7],"each":[8],"other":[9],"over":[10],"different":[11],"telecom":[12,16],"operators.":[13],"Thus,":[14],"for":[15],"operators,":[17],"how":[18],"to":[19,33,48,55,89,147,160,169],"attract":[20],"new":[21],"customers":[22],"is":[23,32,47],"a":[24,36,79,96],"significant":[25],"issue.":[26],"The":[27,181],"work":[28,46],"of":[29,105,136,183,199],"churn":[30,43],"prediction":[31],"determine":[34,170],"whether":[35],"customer":[37],"would":[38],"leave":[39],"soon.":[40],"Differing":[41],"from":[42,59,100,113],"prediction,":[44],"our":[45,184,192],"find":[49],"those":[50],"who":[52],"are":[53,69,126],"likely":[54],"join":[56],"target":[57],"services":[58],"the":[60,63,91,101,110,119,144,149,154,167,171,188,197],"competitors":[61],"in":[62,118],"near":[64],"future,":[65],"where":[66],"these":[67],"called":[70],"potential":[71,75,114,137,162,172,201],"users.":[72,106,138,173,202],"To":[73],"infer":[74],"users,":[76],"we":[77,94,108,131,142,175],"propose":[78],"framework":[80],"including":[81],"feature":[82,84,140],"extraction,":[83,141],"selection,":[85],"and":[86,157,165],"classifier":[87],"learning":[88],"solve":[90],"problem.":[92],"First,":[93],"construct":[95],"heterogeneous":[97,120],"information":[98,121],"network":[99],"call":[102],"detail":[103],"records":[104],"Then,":[107],"extract":[109,132],"explicit":[111,156],"features":[112,135,159,189],"users'":[115],"interaction":[116],"behavior":[117],"network.":[122],"Moreover,":[123],"because":[124],"influenced":[127],"by":[128,191],"their":[129],"community,":[130],"community-based":[133],"implicit":[134,158],"After":[139],"explore":[143],"Information":[145],"Gain":[146],"select":[148],"effective":[150,155],"features.":[151],"We":[152],"use":[153,166],"learn":[161],"user":[163],"classifiers,":[164],"classifiers":[168],"Finally,":[174],"conduct":[176],"experiments":[177,185],"on":[178],"real":[179],"datasets.":[180],"results":[182],"show":[186],"that":[187],"extracted":[190],"proposed":[193],"method":[194],"improve":[196],"accuracy":[198],"inferring":[200]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
