乐胖代购免代理版

{"id":"https://openalex.org/W3211949929","doi":"https://doi.org/10.1142/s0218194021500455","title":"Methods on Detecting Closely Related Topics and Spatial Events","display_name":"Methods on Detecting Closely Related Topics and Spatial Events","publication_year":2021,"publication_date":"2021-10-01","ids":{"openalex":"https://openalex.org/W3211949929","doi":"https://doi.org/10.1142/s0218194021500455","mag":"3211949929"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218194021500455","pdf_url":null,"source":{"id":"https://openalex.org/S131442419","display_name":"International Journal of Software Engineering and Knowledge Engineering","issn_l":"0218-1940","issn":["0218-1940","1793-6403"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-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/A5086486425","display_name":"Zehao Yu","orcid":"https://orcid.org/0000-0002-7290-8005"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zehao Yu","raw_affiliation_strings":["School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, P.\u00a0R.\u00a0China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, P.\u00a0R.\u00a0China","institution_ids":["https://openalex.org/I139759216"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5086486425"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.103,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.336273,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":57,"max":67},"biblio":{"volume":"31","issue":"10","first_page":"1377","last_page":"1398"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9978,"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":0.9978,"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/T11106","display_name":"Data Management and Algorithms","score":0.993,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9915,"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/perplexity","display_name":"Perplexity","score":0.7918273},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.4687619},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.45305672}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8193319},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.7918273},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.62136996},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5346709},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5141773},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.4687619},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.45869014},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.45305672},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.43057403},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4231109},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32581356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31730232},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.12453589},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218194021500455","pdf_url":null,"source":{"id":"https://openalex.org/S131442419","display_name":"International Journal of Software Engineering and Knowledge Engineering","issn_l":"0218-1940","issn":["0218-1940","1793-6403"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":14,"referenced_works":["https://openalex.org/W1982029265","https://openalex.org/W2240167975","https://openalex.org/W2478394093","https://openalex.org/W2886659698","https://openalex.org/W2901450139","https://openalex.org/W2908395583","https://openalex.org/W2912217870","https://openalex.org/W3025297341","https://openalex.org/W3026872496","https://openalex.org/W3080232144","https://openalex.org/W3090435833","https://openalex.org/W3090882127","https://openalex.org/W3117864197","https://openalex.org/W3123149946"],"related_works":["https://openalex.org/W4387876786","https://openalex.org/W4293734197","https://openalex.org/W4206967254","https://openalex.org/W3117044075","https://openalex.org/W2501642273","https://openalex.org/W2278712165","https://openalex.org/W2169401934","https://openalex.org/W2168471263","https://openalex.org/W2131689821","https://openalex.org/W1498269992"],"abstract_inverted_index":{"Topic":[0],"detection":[1,27],"is":[2],"a":[3,33,89,109,115],"hot":[4],"issue":[5],"that":[6,75,124,152,182],"many":[7],"researchers":[8],"are":[9,67],"interested":[10],"in.":[11],"The":[12],"previous":[13],"researches":[14],"focused":[15],"on":[16,80,147,178],"the":[17,25,133,142],"single":[18],"data":[19,30,45,106],"stream,":[20],"they":[21,37],"did":[22],"not":[23],"consider":[24],"topic":[26,167],"from":[28,43,194],"different":[29,44,105,148,160,179,195],"streams":[31,107],"in":[32,62,108,159],"harmonious":[34],"way,":[35],"so":[36],"cannot":[38,76],"detect":[39,95,132,185],"closely":[40,96,186],"related":[41,97,187],"topics":[42,99,188],"streams.":[46],"Recently,":[47],"Twitter,":[48],"along":[49],"with":[50],"other":[51,156],"SNS":[52],"such":[53,162],"as":[54,128,130,163],"Weibo,":[55],"and":[56,100,150,168,189],"Yelp,":[57],"began":[58],"backing":[59],"position":[60],"services":[61],"their":[63,101],"texts.":[64,143],"Previous":[65],"approaches":[66,158],"either":[68],"complex":[69],"to":[70,131],"be":[71],"conducted":[72],"or":[73],"oversimplified":[74],"achieve":[77],"better":[78],"performance":[79],"detecting":[81],"spatial":[82,134],"topics.":[83],"In":[84],"our":[85,153,174],"paper,":[86],"we":[87],"introduce":[88,114],"probabilistic":[90,116],"method":[91,117],"which":[92],"can":[93,125,137,184],"precisely":[94],"bursty":[98,102,191],"periods":[103,193],"across":[104],"unified":[110],"way.":[111],"We":[112,144],"also":[113,138],"called":[118],"Latent":[119],"Spatial":[120],"Events":[121],"Model":[122],"(LSEM)":[123],"find":[126],"areas":[127],"well":[129],"events,":[135],"it":[136,183],"predict":[139],"positions":[140],"of":[141,166,173],"evaluate":[145],"LSEM":[146],"datasets":[149,180],"reflect":[151],"approach":[154,177],"outperforms":[155],"baseline":[157],"indexes":[161],"perplexity,":[164],"entropy":[165],"KL-divergence,":[169],"range":[170],"error.":[171],"Evaluation":[172],"first":[175],"proposed":[176],"shows":[181],"meaningful":[190],"time":[192],"datasets.":[196]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3211949929","counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-01-07T01:28:16.271910","created_date":"2021-11-22"}