{"id":"https://openalex.org/W3135096059","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892698","title":"Learning Fine-grained Location Embedding from Human Mobility with Graph Neural Networks","display_name":"Learning Fine-grained Location Embedding from Human Mobility with Graph Neural Networks","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W3135096059","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892698","mag":"3135096059"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892698","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892698","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":"2161-4407","issn":["2161-4407"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2103.00483","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022520101","display_name":"Chenyu Tian","orcid":"https://orcid.org/0000-0002-7748-5873"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyu Tian","raw_affiliation_strings":["Tsinghua-Berkeley Shenzhen Institute, Tsinghua University,Shenzhen,China","Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Tsinghua University,Shenzhen,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010328748","display_name":"Yuchun Zhang","orcid":"https://orcid.org/0000-0002-9825-6500"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuchun Zhang","raw_affiliation_strings":["Tencent Inc,Shenzhen,China","Tencent Inc, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc,Shenzhen,China","institution_ids":["https://openalex.org/I2250653659"]},{"raw_affiliation_string":"Tencent Inc, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083593652","display_name":"Zefeng Weng","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zefeng Weng","raw_affiliation_strings":["Tencent Inc,Shenzhen,China","Tencent Inc, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc,Shenzhen,China","institution_ids":["https://openalex.org/I2250653659"]},{"raw_affiliation_string":"Tencent Inc, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109427828","display_name":"Xiusen Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiusen Gu","raw_affiliation_strings":["Tencent Inc,Shenzhen,China","Tencent Inc, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc,Shenzhen,China","institution_ids":["https://openalex.org/I2250653659"]},{"raw_affiliation_string":"Tencent Inc, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108743840","display_name":"Wai Kin Victor Chan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wai Kin Chan","raw_affiliation_strings":["Tsinghua-Berkeley Shenzhen Institute, Tsinghua University,Shenzhen,China","Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Tsinghua University,Shenzhen,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.2276,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.95650162,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998000264167786,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10757","display_name":"Geographic Information Systems Studies","score":0.9794999957084656,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"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":0.7979720830917358},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6101409196853638},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5227851271629333},{"id":"https://openalex.org/keywords/spatial-contextual-awareness","display_name":"Spatial contextual awareness","score":0.5030872225761414},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.5022594928741455},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4652349352836609},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4553452432155609},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4331547021865845},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4151676893234253},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.371112585067749},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37060460448265076},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.15607962012290955}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7979720830917358},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6101409196853638},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5227851271629333},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.5030872225761414},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.5022594928741455},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4652349352836609},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4553452432155609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4331547021865845},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4151676893234253},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.371112585067749},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37060460448265076},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.15607962012290955},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892698","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892698","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":"2161-4407","issn":["2161-4407"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2103.00483","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.00483","pdf_url":"https://arxiv.org/pdf/2103.00483","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2103.00483","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.00483","pdf_url":"https://arxiv.org/pdf/2103.00483","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8299999833106995}],"awards":[{"id":"https://openalex.org/G6298785130","display_name":"\u591a\u5c42\u793e\u4f1a\u7f51\u7edc\u5728\u9690\u79c1\u4fdd\u62a4\u6761\u4ef6\u4e0b\u7684\u5efa\u6a21\u53ca\u7279\u6027\u7814\u7a76\u4e0e\u5e94\u7528","funder_award_id":"71971127","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G779962718","display_name":null,"funder_award_id":"JCYJ20210324135011030,WDZC20200818121348001","funder_id":"https://openalex.org/F4320326705","funder_display_name":"Science, Technology and Innovation Commission of Shenzhen Municipality"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320326705","display_name":"Science, Technology and Innovation Commission of Shenzhen Municipality","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W1662382123","https://openalex.org/W2519887557","https://openalex.org/W2554952599","https://openalex.org/W2573719245","https://openalex.org/W2604411573","https://openalex.org/W2741206673","https://openalex.org/W2767923185","https://openalex.org/W2768009948","https://openalex.org/W2776890924","https://openalex.org/W2788114581","https://openalex.org/W2795016801","https://openalex.org/W2807855639","https://openalex.org/W2807954821","https://openalex.org/W2820951506","https://openalex.org/W2950577311","https://openalex.org/W2962756421","https://openalex.org/W2962949580","https://openalex.org/W2964015378","https://openalex.org/W2982748034","https://openalex.org/W2987232971","https://openalex.org/W3005440315","https://openalex.org/W3007832212","https://openalex.org/W3034277777","https://openalex.org/W3135694489","https://openalex.org/W4294170691","https://openalex.org/W4297571622","https://openalex.org/W6636510571","https://openalex.org/W6637178625","https://openalex.org/W6682691769","https://openalex.org/W6726873649","https://openalex.org/W6732130061","https://openalex.org/W6772454105","https://openalex.org/W6774505149"],"related_works":["https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2912814903","https://openalex.org/W2123605750","https://openalex.org/W2088740331","https://openalex.org/W3038102983","https://openalex.org/W2950907416","https://openalex.org/W1559483280","https://openalex.org/W2082479932","https://openalex.org/W2932872266"],"abstract_inverted_index":{"An":[0],"increasing":[1],"amount":[2],"of":[3,64,132],"location-based":[4],"service":[5],"data":[6,80],"is":[7,120],"being":[8],"accumulated":[9],"and":[10,16,35,78,101,108,127,139],"helps":[11],"to":[12,31,42,84,122],"study":[13],"urban":[14,33],"dynamics":[15],"human":[17,23,102],"mobility.":[18,103],"Location":[19],"embedding":[20,48],"generated":[21],"from":[22,74],"mobility":[24],"trajectories":[25],"has":[26],"become":[27],"a":[28,61,90,105,109,129],"popular":[29],"topic":[30],"understand":[32],"functionality,":[34],"could":[36],"be":[37],"applied":[38],"as":[39],"essential":[40],"resources":[41],"various":[43],"downstream":[44],"tasks.":[45],"Existing":[46],"location":[47,87],"methods":[49],"are":[50,57,149],"mostly":[51],"tailored":[52],"for":[53],"specific":[54],"problems":[55],"that":[56,144],"taken":[58],"place":[59],"within":[60],"small":[62],"group":[63],"areas.":[65],"Downscaling":[66],"the":[67],"spatial":[68,99,110,135],"resolution":[69],"makes":[70],"existing":[71],"approaches":[72],"suffer":[73],"extensive":[75],"computational":[76],"cost":[77],"significant":[79],"sparsity.":[81],"We":[82],"propose":[83],"learn":[85],"finegrained":[86],"representations":[88,145],"through":[89],"GCN-aided":[91],"skip-gram":[92],"model":[93],"named":[94],"GCN-L2V":[95,119,148],"by":[96,147],"considering":[97],"both":[98],"adjacency":[100],"With":[104],"flow":[106],"graph":[107],"graph,":[111],"it":[112],"embeds":[113],"context":[114],"information":[115],"into":[116],"vector":[117],"representations.":[118],"able":[121],"capture":[123],"relationships":[124],"among":[125],"locations":[126],"provides":[128],"better":[130],"notion":[131],"similarity":[133],"in":[134],"environment.":[136],"Quantitative":[137],"experiments":[138],"case":[140],"studies":[141],"empirically":[142],"demonstrate":[143],"learned":[146],"effective.":[150]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2021-03-15T00:00:00"}