{"id":"https://openalex.org/W4400247865","doi":"https://doi.org/10.1108/ijicc-01-2024-0017","title":"Dynamic community detection algorithm based on hyperbolic graph convolution","display_name":"Dynamic community detection algorithm based on hyperbolic graph convolution","publication_year":2024,"publication_date":"2024-07-02","ids":{"openalex":"https://openalex.org/W4400247865","doi":"https://doi.org/10.1108/ijicc-01-2024-0017"},"language":"en","primary_location":{"id":"doi:10.1108/ijicc-01-2024-0017","is_oa":false,"landing_page_url":"https://doi.org/10.1108/ijicc-01-2024-0017","pdf_url":null,"source":{"id":"https://openalex.org/S124503262","display_name":"International Journal of Intelligent Computing and Cybernetics","issn_l":"1756-378X","issn":["1756-378X","1756-3798"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Intelligent Computing and Cybernetics","raw_type":"journal-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/A5009337045","display_name":"Weijiang Wu","orcid":"https://orcid.org/0009-0004-4087-5693"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weijiang Wu","raw_affiliation_strings":["Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum Beijing, Beijing, China","College of Information Science and Engineering/College of Artificial Intelligence, China University of Petroleum Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-4087-5693","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum Beijing, Beijing, China","institution_ids":["https://openalex.org/I204553293"]},{"raw_affiliation_string":"College of Information Science and Engineering/College of Artificial Intelligence, China University of Petroleum Beijing, Beijing, China","institution_ids":["https://openalex.org/I204553293","https://openalex.org/I4210100255"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062175098","display_name":"He-Ping Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heping Tan","raw_affiliation_strings":["Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum Beijing, Beijing, China","College of Information Science and Engineering/College of Artificial Intelligence, China University of Petroleum Beijing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum Beijing, Beijing, China","institution_ids":["https://openalex.org/I204553293"]},{"raw_affiliation_string":"College of Information Science and Engineering/College of Artificial Intelligence, China University of Petroleum Beijing, Beijing, China","institution_ids":["https://openalex.org/I204553293","https://openalex.org/I4210100255"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017426295","display_name":"Yifeng Zheng","orcid":"https://orcid.org/0000-0001-9884-2481"},"institutions":[{"id":"https://openalex.org/I9356336","display_name":"Minnan Normal University","ror":"https://ror.org/02vj1vm13","country_code":"CN","type":"education","lineage":["https://openalex.org/I9356336"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifeng Zheng","raw_affiliation_strings":["School of Computer Science, Minnan Normal University, Zhangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9884-2481","affiliations":[{"raw_affiliation_string":"School of Computer Science, Minnan Normal University, Zhangzhou, China","institution_ids":["https://openalex.org/I9356336"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5009337045"],"corresponding_institution_ids":["https://openalex.org/I204553293","https://openalex.org/I4210100255"],"apc_list":null,"apc_paid":null,"fwci":0.2008,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.44648236,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"17","issue":"3","first_page":"632","last_page":"653"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9818999767303467,"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"}},{"id":"https://openalex.org/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.9745000004768372,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7250041365623474},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5935788750648499},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5829756855964661},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5020654201507568},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3230651021003723},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2101079225540161},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.0707324743270874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7250041365623474},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5935788750648499},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5829756855964661},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5020654201507568},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3230651021003723},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2101079225540161},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0707324743270874}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/ijicc-01-2024-0017","is_oa":false,"landing_page_url":"https://doi.org/10.1108/ijicc-01-2024-0017","pdf_url":null,"source":{"id":"https://openalex.org/S124503262","display_name":"International Journal of Intelligent Computing and Cybernetics","issn_l":"1756-378X","issn":["1756-378X","1756-3798"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Intelligent Computing and Cybernetics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W204109643","https://openalex.org/W1522301498","https://openalex.org/W1603920809","https://openalex.org/W1757977160","https://openalex.org/W1948772614","https://openalex.org/W1968656203","https://openalex.org/W2022686119","https://openalex.org/W2023666652","https://openalex.org/W2033403400","https://openalex.org/W2052639453","https://openalex.org/W2058600246","https://openalex.org/W2100741829","https://openalex.org/W2151936673","https://openalex.org/W2154851992","https://openalex.org/W2166660524","https://openalex.org/W2618798060","https://openalex.org/W2767849480","https://openalex.org/W2771870004","https://openalex.org/W2890177507","https://openalex.org/W2898221481","https://openalex.org/W2914648904","https://openalex.org/W2925436546","https://openalex.org/W2955067198","https://openalex.org/W3015583272","https://openalex.org/W3106390811","https://openalex.org/W3184141547","https://openalex.org/W3214706508","https://openalex.org/W4289133677","https://openalex.org/W4318041120","https://openalex.org/W4367360708","https://openalex.org/W4377236721","https://openalex.org/W4385568361","https://openalex.org/W4387266502","https://openalex.org/W4387575642","https://openalex.org/W4387766245","https://openalex.org/W4387824552","https://openalex.org/W4390502223","https://openalex.org/W4407354035"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Purpose":[0],"Community":[1],"detection":[2,19,51,160],"is":[3,54],"a":[4,43,174,208],"key":[5],"factor":[6],"in":[7,36,161,171,177,236,267],"analyzing":[8],"the":[9,26,60,64,70,76,90,109,114,119,123,127,132,156,192,201,204,214,231,250,256,276],"structural":[10],"features":[11,62],"of":[12,28,101,122,134,158,169,203,234],"complex":[13,149,186],"networks.":[14,163],"However,":[15],"traditional":[16,183],"dynamic":[17,46],"community":[18,50,110,115,159],"methods":[20,221],"often":[21],"fail":[22],"to":[23,81,182,261],"effectively":[24,262],"solve":[25],"problems":[27],"deep":[29],"network":[30,49],"information":[31,86,104,233],"loss":[32],"and":[33,67,78,85,98,126,142,173,217,242],"computational":[34],"complexity":[35],"hyperbolic":[37,44,65,71,196,243,257,268],"space.":[38,280],"To":[39,130],"address":[40],"this":[41],"challenge,":[42],"space-based":[45],"graph":[47,72,244],"neural":[48],"model":[52,194,229,260],"(HSDCDM)":[53],"proposed.":[55],"Design/methodology/approach":[56],"HSDCDM":[57,153,193],"first":[58],"projects":[59],"node":[61,120],"into":[63],"space":[66,124,237,258],"then":[68],"utilizes":[69],"convolution":[73],"module":[74,95,112],"on":[75,139,148,223,255,278],"Poincar\u00e9":[77],"Lorentz":[79,259],"models":[80],"realize":[82],"feature":[83],"fusion":[84],"transfer.":[87],"In":[88],"addition,":[89],"parallel":[91],"optimized":[92],"temporal":[93],"memory":[94],"ensures":[96],"fast":[97],"accurate":[99],"capture":[100],"time":[102,128,264],"domain":[103,125],"over":[105,220],"extended":[106],"periods.":[107],"Finally,":[108],"clustering":[111],"divides":[113],"structure":[116],"by":[117,274],"combining":[118],"characteristics":[121],"domain.":[129],"evaluate":[131],"performance":[133],"HSDCDM,":[135],"experiments":[136],"are":[137],"conducted":[138],"both":[140],"artificial":[141],"real":[143],"datasets.":[144],"Findings":[145],"Experimental":[146],"results":[147],"networks":[150,187],"demonstrate":[151],"that":[152,212],"significantly":[154],"enhances":[155],"quality":[157],"hierarchical":[162],"It":[164],"shows":[165],"an":[166],"average":[167],"improvement":[168],"7.29%":[170],"NMI":[172],"9.07%":[175],"increase":[176],"ARI":[178],"across":[179],"datasets":[180],"compared":[181],"methods.":[184,226],"For":[185],"with":[188],"non-Euclidean":[189],"geometric":[190],"structures,":[191],"incorporating":[195],"geometry":[197,225],"can":[198],"better":[199],"handle":[200],"discontinuity":[202],"metric":[205],"space,":[206,269],"provides":[207],"more":[209],"compact":[210],"embedding":[211],"preserves":[213],"data":[215,266],"structure,":[216],"offers":[218],"advantages":[219],"based":[222],"Euclidean":[224],"Originality/value":[227],"This":[228],"aggregates":[230],"potential":[232],"nodes":[235],"through":[238],"manifold-preserving":[239],"distribution":[240],"mapping":[241],"topology":[245],"modules.":[246],"Moreover,":[247],"it":[248],"optimizes":[249],"Simple":[251],"Recurrent":[252],"Unit":[253],"(SRU)":[254],"extract":[263],"series":[265],"thereby":[270],"enhancing":[271],"computing":[272],"efficiency":[273],"eliminating":[275],"reliance":[277],"tangent":[279]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}