{"id":"https://openalex.org/W3008381538","doi":"https://doi.org/10.1109/bigdata47090.2019.9006428","title":"Exploiting Graph Convolutional Networks for Representation Learning of Mobile App Usage","display_name":"Exploiting Graph Convolutional Networks for Representation Learning of Mobile App Usage","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3008381538","doi":"https://doi.org/10.1109/bigdata47090.2019.9006428","mag":"3008381538"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5109472441","display_name":"Keiichi Ochiai","orcid":"https://orcid.org/0000-0001-8344-0551"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Keiichi Ochiai","raw_affiliation_strings":["NTT DOCOMO.INC, Japan"],"affiliations":[{"raw_affiliation_string":"NTT DOCOMO.INC, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071299671","display_name":"Naoki Yamamoto","orcid":"https://orcid.org/0000-0002-8497-4608"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Naoki Yamamoto","raw_affiliation_strings":["NTT DOCOMO.INC, Japan"],"affiliations":[{"raw_affiliation_string":"NTT DOCOMO.INC, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086463689","display_name":"Takashi Hamatani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Takashi Hamatani","raw_affiliation_strings":["NTT DOCOMO.INC, Japan"],"affiliations":[{"raw_affiliation_string":"NTT DOCOMO.INC, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029670252","display_name":"Yusuke Fukazawa","orcid":"https://orcid.org/0000-0001-9834-9339"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yusuke Fukazawa","raw_affiliation_strings":["NTT DOCOMO.INC, Japan"],"affiliations":[{"raw_affiliation_string":"NTT DOCOMO.INC, Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057962325","display_name":"Takayasu Yamaguchi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Takayasu Yamaguchi","raw_affiliation_strings":["NTT DOCOMO.INC, Japan"],"affiliations":[{"raw_affiliation_string":"NTT DOCOMO.INC, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5109472441"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.242,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.58311313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"2019","issue":null,"first_page":"5379","last_page":"5383"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12238","display_name":"Green IT and Sustainability","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T12238","display_name":"Green IT and Sustainability","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.989799976348877,"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"}},{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.984000027179718,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.8421471118927002},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6807656288146973},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5783793330192566},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5536817908287048},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4595893621444702},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45623114705085754},{"id":"https://openalex.org/keywords/mobile-apps","display_name":"Mobile apps","score":0.44176238775253296},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4329460859298706},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42491716146469116},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3531007170677185},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.18627730011940002}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8421471118927002},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6807656288146973},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5783793330192566},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5536817908287048},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4595893621444702},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45623114705085754},{"id":"https://openalex.org/C2988145974","wikidata":"https://www.wikidata.org/wiki/Q620615","display_name":"Mobile apps","level":2,"score":0.44176238775253296},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4329460859298706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42491716146469116},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3531007170677185},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.18627730011940002},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"mag:3085451098","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002239848288094","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"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/W114517082","https://openalex.org/W1985541164","https://openalex.org/W2060118520","https://openalex.org/W2064675550","https://openalex.org/W2133589238","https://openalex.org/W2136655611","https://openalex.org/W2156648337","https://openalex.org/W2285072859","https://openalex.org/W2301095666","https://openalex.org/W2509156994","https://openalex.org/W2775819279","https://openalex.org/W2919115771","https://openalex.org/W2919692939","https://openalex.org/W2929944287","https://openalex.org/W2953271256","https://openalex.org/W2962711740","https://openalex.org/W2962767366","https://openalex.org/W2963953172","https://openalex.org/W2964015378","https://openalex.org/W4294558607","https://openalex.org/W6679997575","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6754929296","https://openalex.org/W6759941353"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W3048601286","https://openalex.org/W2965925734","https://openalex.org/W4285218279"],"abstract_inverted_index":{"We":[0,97],"propose":[1],"to":[2,7,24,104],"apply":[3],"graph":[4,77,94],"convolutional":[5,78,95],"networks":[6],"a":[8,85,115],"novel":[9],"domain,":[10],"mobile":[11,90],"app":[12,74,91],"usage.":[13],"User":[14],"state":[15,57],"estimation":[16,58],"from":[17],"smartphone":[18],"usage":[19,42,92],"has":[20],"attracted":[21],"attention":[22],"thanks":[23],"the":[25,33,36,49,53,64,70,99],"prevalence":[26],"of":[27,35,38,55,66],"smartphones.":[28],"Basic":[29],"statistics":[30],"such":[31],"as":[32],"sum":[34],"number":[37],"used":[39,46,68],"apps":[40,67],"and":[41,69],"duration":[43],"have":[44],"been":[45],"for":[47,89,114],"estimating":[48],"user":[50,56],"state.":[51],"However,":[52],"accuracy":[54],"can":[59],"be":[60],"improved":[61],"by":[62],"considering":[63],"sequence":[65],"relationship":[71],"between":[72],"each":[73],"based":[75],"on":[76],"networks.":[79,96],"In":[80],"this":[81],"paper,":[82],"we":[83],"proposed":[84,100],"representation":[86],"learning":[87,107],"method":[88,101],"using":[93],"evaluate":[98],"through":[102],"comparison":[103],"another":[105],"deep":[106],"approach,":[108],"i.":[109],"e.,":[110],"long":[111],"short-term":[112],"memory,":[113],"classification":[116],"problem.":[117]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
