{"id":"https://openalex.org/W2997427823","doi":"https://doi.org/10.1108/lht-11-2018-0179","title":"Constructing the social network prediction model based on data mining and link prediction analysis","display_name":"Constructing the social network prediction model based on data mining and link prediction analysis","publication_year":2019,"publication_date":"2019-11-29","ids":{"openalex":"https://openalex.org/W2997427823","doi":"https://doi.org/10.1108/lht-11-2018-0179","mag":"2997427823"},"language":"en","primary_location":{"id":"doi:10.1108/lht-11-2018-0179","is_oa":false,"landing_page_url":"https://doi.org/10.1108/lht-11-2018-0179","pdf_url":null,"source":{"id":"https://openalex.org/S143568823","display_name":"Library Hi Tech","issn_l":"0737-8831","issn":["0737-8831","2054-166X"],"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":"Library Hi Tech","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/A5007202521","display_name":"Yuxian Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I2802249531","display_name":"Shenyang Conservatory of Music","ror":"https://ror.org/04z3jpx43","country_code":"CN","type":"education","lineage":["https://openalex.org/I2802249531"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuxian Gao","raw_affiliation_strings":["Shenyang Conservatory of Music, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Shenyang Conservatory of Music, Shenyang, China","institution_ids":["https://openalex.org/I2802249531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5007202521"],"corresponding_institution_ids":["https://openalex.org/I2802249531"],"apc_list":null,"apc_paid":null,"fwci":0.8688,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.74353794,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"38","issue":"2","first_page":"320","last_page":"333"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9990000128746033,"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":0.9990000128746033,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11122","display_name":"Online Learning and Analytics","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.7952653169631958},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6943467855453491},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.6085736155509949},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5639995336532593},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5055830478668213},{"id":"https://openalex.org/keywords/link-analysis","display_name":"Link analysis","score":0.45453953742980957},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3586868941783905},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3028862476348877},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.19283917546272278}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7952653169631958},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6943467855453491},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.6085736155509949},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5639995336532593},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5055830478668213},{"id":"https://openalex.org/C1173588","wikidata":"https://www.wikidata.org/wiki/Q6554294","display_name":"Link analysis","level":2,"score":0.45453953742980957},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3586868941783905},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3028862476348877},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.19283917546272278},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/lht-11-2018-0179","is_oa":false,"landing_page_url":"https://doi.org/10.1108/lht-11-2018-0179","pdf_url":null,"source":{"id":"https://openalex.org/S143568823","display_name":"Library Hi Tech","issn_l":"0737-8831","issn":["0737-8831","2054-166X"],"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":"Library Hi Tech","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1972436494","https://openalex.org/W2137180616","https://openalex.org/W2179199242","https://openalex.org/W2208595703","https://openalex.org/W2212381007","https://openalex.org/W2241240972","https://openalex.org/W2253276617","https://openalex.org/W2255539840","https://openalex.org/W2286513919","https://openalex.org/W2333559586","https://openalex.org/W2564701426","https://openalex.org/W2584419678","https://openalex.org/W2586769710","https://openalex.org/W2610963935","https://openalex.org/W2672698676","https://openalex.org/W2750203123","https://openalex.org/W2760051845","https://openalex.org/W2796423064","https://openalex.org/W2964126306","https://openalex.org/W2964140784"],"related_works":["https://openalex.org/W2966207284","https://openalex.org/W2250140425","https://openalex.org/W2734587838","https://openalex.org/W2091018730","https://openalex.org/W4280583453","https://openalex.org/W2099940443","https://openalex.org/W2389064843","https://openalex.org/W2078736197","https://openalex.org/W2098669189","https://openalex.org/W2032501302"],"abstract_inverted_index":{"Purpose":[0],"The":[1,116,241,254,399],"purpose":[2],"of":[3,19,26,37,76,94,104,147,171,195,211,217,243,299,307,327,338,344,350,367,370,386,414,418],"this":[4,32,244,288,328],"paper":[5],"is":[6,246,258,279,293,309,342,362,402,417],"to":[7,11,15,64,85,100,110,143,175,200,267,295,357,364,377],"apply":[8],"link":[9,20,81,154,219,248,332,360,375,390],"prediction":[10,21,122,155,220,249,256,314,361,376],"community":[12,378],"mining":[13],"and":[14,54,58,166,169,189,226,229,269,281,301,325,396,405],"clarify":[16],"the":[17,24,34,50,66,74,80,87,92,95,102,105,112,121,144,150,153,162,167,172,184,193,205,209,215,218,236,265,275,290,302,312,323,365,406,423],"role":[18],"in":[22,149,208,250,272,287,311],"improving":[23],"performance":[25,368],"social":[27,251,339,351,371],"network":[28,252,340,352,372],"analysis.":[29,253,353,398],"Design/methodology/approach":[30],"In":[31,182,330],"study,":[33,289],"2009":[35],"version":[36],"Enron":[38],"e-mail":[39],"data":[40,151,164,320,392],"set":[41],"provided":[42],"by":[43,125,135,373],"Carnegie":[44],"Mellon":[45],"University":[46],"was":[47,83,97],"selected":[48],"as":[49,109,334,389],"research":[51,106,185,228,245,400],"object":[52],"first,":[53],"bibliometric":[55],"analysis":[56,60,369,395],"method":[57,61,156,173,221],"citation":[59,397],"were":[62],"adopted":[63,84,99],"compare":[65],"differences":[67],"between":[68,264,277,304],"various":[69,77],"studies.":[70],"Second,":[71],"based":[72,259],"on":[73,161,186,235,247,260,318],"impact":[75],"interpersonal":[78],"relationships,":[79],"model":[82,257,292],"analyze":[86],"relationship":[88,263,276],"among":[89],"people.":[90],"Finally,":[91,316],"factorization":[93],"matrix":[96],"further":[98,177,202,227],"obtain":[101],"characteristics":[103],"object,":[107],"so":[108],"predict":[111,268],"unknown":[113],"relationship.":[114,139],"Findings":[115],"experimental":[117,407],"results":[118,123,408],"show":[119],"that":[120,359],"obtained":[124,134,409],"considering":[126,136],"multiple":[127],"relationships":[128,283],"are":[129,284],"more":[130],"accurate":[131],"than":[132],"those":[133],"only":[137],"one":[138],"Research":[140],"limitations/implications":[141],"Due":[142],"limited":[145],"number":[146],"objects":[148,266],"set,":[152,165],"has":[157],"not":[158],"been":[159],"tested":[160],"large-scale":[163],"validity":[168],"correctness":[170],"need":[174,199],"be":[176,201,232],"verified":[178],"with":[179],"larger":[180],"data.":[181,238],"addition,":[183,331],"algorithm":[187,190],"complexity":[188],"optimization,":[191],"including":[192],"storage":[194],"sparse":[196,213,237],"matrix,":[197],"also":[198,343],"studied.":[203],"At":[204],"same":[206],"time,":[207],"case":[210],"extremely":[212],"data,":[214],"accuracy":[216,326],"will":[222],"decline":[223],"a":[224,261,384,411],"lot,":[225],"discussion":[230],"should":[231],"carried":[233],"out":[234],"Practical":[239],"implications":[240],"focus":[242],"traditional":[255],"certain":[262,412,419],"analyze,":[270],"but":[271],"real":[273,319],"life,":[274],"people":[278],"diverse,":[280],"different":[282,297,305],"interactive.":[285],"Therefore,":[286],"graph":[291],"used":[294],"express":[296],"kinds":[298,306],"relations,":[300],"influence":[303],"relations":[308],"considered":[310],"actual":[313],"process.":[315],"experiments":[317],"sets":[321],"prove":[322,358],"effectiveness":[324],"method.":[329],"prediction,":[333,391],"an":[335],"important":[336],"part":[337],"analysis,":[341],"great":[345],"significance":[346],"for":[347,422],"other":[348],"applications":[349],"This":[354,381],"study":[355,382],"attempts":[356],"helpful":[363],"improvement":[366],"applying":[374],"mining.":[379],"Originality/value":[380],"adopts":[383],"variety":[385],"methods,":[387],"such":[388],"mining,":[393],"literature":[394],"direction":[401],"relatively":[403],"new,":[404],"have":[410],"degree":[413],"credibility,":[415],"which":[416],"reference":[420],"value":[421],"following":[424],"related":[425],"research.":[426]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
