{"id":"https://openalex.org/W7125789666","doi":"https://doi.org/10.1109/access.2026.3657961","title":"Hybrid GNN-Based Link Prediction Model for Identifying Drug-Related Organized Crime Groups on Twitter","display_name":"Hybrid GNN-Based Link Prediction Model for Identifying Drug-Related Organized Crime Groups on Twitter","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7125789666","doi":"https://doi.org/10.1109/access.2026.3657961"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3657961","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3657961","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3657961","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123976631","display_name":"Eun-Young Park","orcid":null},"institutions":[{"id":"https://openalex.org/I9323808","display_name":"Daegu University","ror":"https://ror.org/01zqccq48","country_code":"KR","type":"education","lineage":["https://openalex.org/I9323808"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Eun-Young Park","raw_affiliation_strings":["Department of Computer Engineering, Daegu University, Gyeongsan, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Daegu University, Gyeongsan, South Korea","institution_ids":["https://openalex.org/I9323808"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123898560","display_name":"Hyeon-Woo Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I9323808","display_name":"Daegu University","ror":"https://ror.org/01zqccq48","country_code":"KR","type":"education","lineage":["https://openalex.org/I9323808"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyeon-Woo Lee","raw_affiliation_strings":["Department of Computer Engineering, Daegu University, Gyeongsan, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Daegu University, Gyeongsan, South Korea","institution_ids":["https://openalex.org/I9323808"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123926232","display_name":"Jiyeon Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I9323808","display_name":"Daegu University","ror":"https://ror.org/01zqccq48","country_code":"KR","type":"education","lineage":["https://openalex.org/I9323808"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jiyeon Kim","raw_affiliation_strings":["Department of Computer Engineering, Daegu University, Gyeongsan, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Daegu University, Gyeongsan, South Korea","institution_ids":["https://openalex.org/I9323808"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5123976631"],"corresponding_institution_ids":["https://openalex.org/I9323808"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41554527,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"15044","last_page":"15063"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.5151000022888184,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.5151000022888184,"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/T12519","display_name":"Cybercrime and Law Enforcement Studies","score":0.11050000041723251,"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"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.09719999879598618,"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/homogeneous","display_name":"Homogeneous","score":0.5460000038146973},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5378000140190125},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.48570001125335693},{"id":"https://openalex.org/keywords/link-analysis","display_name":"Link analysis","score":0.4101000130176544},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.3873000144958496},{"id":"https://openalex.org/keywords/heterogeneous-network","display_name":"Heterogeneous network","score":0.36559998989105225},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.35679998993873596},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.3447999954223633},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.33340001106262207}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7846999764442444},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.5460000038146973},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5378000140190125},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.48570001125335693},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.414000004529953},{"id":"https://openalex.org/C1173588","wikidata":"https://www.wikidata.org/wiki/Q6554294","display_name":"Link analysis","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.3873000144958496},{"id":"https://openalex.org/C158207573","wikidata":"https://www.wikidata.org/wiki/Q5747224","display_name":"Heterogeneous network","level":4,"score":0.36559998989105225},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3571999967098236},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35100001096725464},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34139999747276306},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.33340001106262207},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3043999969959259},{"id":"https://openalex.org/C4727928","wikidata":"https://www.wikidata.org/wiki/Q17164759","display_name":"Social network (sociolinguistics)","level":3,"score":0.30079999566078186},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.2825999855995178},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C234837","wikidata":"https://www.wikidata.org/wiki/Q1420493","display_name":"Conceptual graph","level":3,"score":0.2718000113964081},{"id":"https://openalex.org/C87414783","wikidata":"https://www.wikidata.org/wiki/Q1002603","display_name":"Degree distribution","level":3,"score":0.2705000042915344},{"id":"https://openalex.org/C2777889803","wikidata":"https://www.wikidata.org/wiki/Q25047676","display_name":"Named entity","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3657961","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3657961","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3657961","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3657961","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.784446120262146,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W4295925600","https://openalex.org/W4296143215","https://openalex.org/W4386965067","https://openalex.org/W4391557659","https://openalex.org/W4392265234","https://openalex.org/W4395683274","https://openalex.org/W4395957340","https://openalex.org/W4400811631","https://openalex.org/W4400999562","https://openalex.org/W4401567681","https://openalex.org/W4401664775","https://openalex.org/W4402761418","https://openalex.org/W4402808383","https://openalex.org/W4403256071","https://openalex.org/W4404172770","https://openalex.org/W4404524465","https://openalex.org/W4405441946","https://openalex.org/W4406080771","https://openalex.org/W4406269399","https://openalex.org/W4407130860","https://openalex.org/W4408800807","https://openalex.org/W4409468609","https://openalex.org/W4409814790","https://openalex.org/W4409883407","https://openalex.org/W4411326193","https://openalex.org/W4411466566","https://openalex.org/W4412402120","https://openalex.org/W4412746171","https://openalex.org/W4414293065"],"related_works":[],"abstract_inverted_index":{"In":[0,100],"this":[1,34],"paper,":[2],"we":[3,36,56,117,128],"propose":[4],"a":[5,58],"Graph":[6,133,137,142],"Neural":[7],"Network":[8,135,139,144],"(GNN)-based":[9],"link":[10,114,126,211],"prediction":[11,115,212],"model":[12,183,196],"to":[13,28,66,96,102],"analyze":[14],"the":[15,74,80,91,97,104,160,164,181,185,189,193,202],"interconnections":[16],"of":[17,93,106,162],"Drug-related":[18],"Organized":[19],"Crime":[20],"Groups":[21],"(DOCG)":[22],"on":[23,113,221],"X":[24],"(formerly":[25],"Twitter)":[26],"and":[27,51,72,79,121,141,178,199],"identify":[29],"their":[30],"potential":[31],"associations.":[32],"To":[33],"end,":[35],"collected":[37],"approximately":[38],"one":[39],"million":[40],"tweets":[41,63],"by":[42],"employing":[43],"slang":[44],"terms":[45],"commonly":[46],"used":[47],"for":[48,168],"drug":[49],"distribution":[50],"promotion.":[52],"From":[53],"these":[54,77],"data,":[55],"constructed":[57],"knowledge":[59,123],"graph":[60],"in":[61,83,215],"which":[62],"containing":[64],"URLs":[65],"secure":[67],"messengers":[68],"such":[69],"as":[70,87,151,153],"Telegram":[71],"Snapchat,":[73],"accounts":[75,94],"posting":[76],"tweets,":[78],"hashtags":[81],"included":[82],"them":[84],"were":[85],"modeled":[86],"nodes,":[88],"thereby":[89],"enabling":[90],"identification":[92],"belonging":[95],"same":[98],"DOCG.":[99],"particular,":[101],"compare":[103],"impact":[105],"distinguishing":[107],"node":[108],"types":[109],"(tweets,":[110],"accounts,":[111],"hashtags)":[112],"performance,":[116],"generated":[118],"both":[119,176],"homogeneous":[120,177],"heterogeneous":[122,179,194],"graphs.":[124],"For":[125],"prediction,":[127],"employed":[129],"three":[130],"GNN":[131,167],"architectures:":[132],"Convolutional":[134],"(GCN),":[136],"Attention":[138],"(GAT),":[140],"Isomorphism":[143],"(GIN).":[145],"We":[146],"further":[147],"developed":[148],"single-model":[149],"frameworks":[150],"well":[152],"hybrid":[154,195],"models":[155],"combining":[156,197],"two":[157],"architectures,":[158],"with":[159],"objective":[161],"determining":[163],"most":[165],"effective":[166,214],"DOCG":[169,220],"identification.":[170],"Experimental":[171],"results":[172],"demonstrate":[173],"that,":[174],"across":[175],"settings,":[180],"GAT":[182,198],"achieved":[184],"highest":[186],"F1-score":[187],"among":[188,219],"single":[190],"models,":[191],"while":[192],"GIN":[200],"yielded":[201],"best":[203],"overall":[204],"performance.":[205],"These":[206],"findings":[207],"indicate":[208],"that":[209],"GNN-based":[210],"is":[213],"detecting":[216],"latent":[217],"associations":[218],"social":[222],"media":[223],"platforms.":[224]},"counts_by_year":[],"updated_date":"2026-02-04T23:10:29.248076","created_date":"2026-01-28T00:00:00"}
