{"id":"https://openalex.org/W7130725676","doi":"https://doi.org/10.1109/tkde.2026.3666727","title":"MgSAN: Multi-Graph Semantic-Aware Adaptive Graph Convolutional Network for Fake News Detection","display_name":"MgSAN: Multi-Graph Semantic-Aware Adaptive Graph Convolutional Network for Fake News Detection","publication_year":2026,"publication_date":"2026-02-20","ids":{"openalex":"https://openalex.org/W7130725676","doi":"https://doi.org/10.1109/tkde.2026.3666727"},"language":null,"primary_location":{"id":"doi:10.1109/tkde.2026.3666727","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2026.3666727","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","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/A5126449779","display_name":"Linlin Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Linlin Zhu","raw_affiliation_strings":["School of Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0001-7312-6534","affiliations":[{"raw_affiliation_string":"School of Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121798816","display_name":"Liang He","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang He","raw_affiliation_strings":["School of Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-6463-5158","affiliations":[{"raw_affiliation_string":"School of Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121763828","display_name":"Heli Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heli Sun","raw_affiliation_strings":["School of Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-2765-6582","affiliations":[{"raw_affiliation_string":"School of Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126503743","display_name":"Qi Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Zhang","raw_affiliation_strings":["School of Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0009-0001-5277-0694","affiliations":[{"raw_affiliation_string":"School of Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5126449779"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31160574,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"38","issue":"5","first_page":"2954","last_page":"2967"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.9828000068664551,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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/T11147","display_name":"Misinformation and Its Impacts","score":0.9828000068664551,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.003000000026077032,"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/T14347","display_name":"Big Data and Digital Economy","score":0.002199999988079071,"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/graph","display_name":"Graph","score":0.6039000153541565},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5023000240325928},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5006999969482422},{"id":"https://openalex.org/keywords/semantic-web","display_name":"Semantic Web","score":0.49380001425743103},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.3628999888896942},{"id":"https://openalex.org/keywords/social-semantic-web","display_name":"Social Semantic Web","score":0.34689998626708984},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.33980000019073486},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.31060001254081726}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8792999982833862},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6039000153541565},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5199999809265137},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5023000240325928},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5006999969482422},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.49380001425743103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.382099986076355},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3628999888896942},{"id":"https://openalex.org/C534406577","wikidata":"https://www.wikidata.org/wiki/Q7550843","display_name":"Social Semantic Web","level":3,"score":0.34689998626708984},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.3000999987125397},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C166423231","wikidata":"https://www.wikidata.org/wiki/Q1891170","display_name":"Semantic search","level":3,"score":0.2953000068664551},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.29120001196861267},{"id":"https://openalex.org/C2777522414","wikidata":"https://www.wikidata.org/wiki/Q648457","display_name":"Social graph","level":3,"score":0.2856000065803528},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26339998841285706},{"id":"https://openalex.org/C167379230","wikidata":"https://www.wikidata.org/wiki/Q1026884","display_name":"Semantic Web Stack","level":3,"score":0.2606000006198883},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2572000026702881},{"id":"https://openalex.org/C503923677","wikidata":"https://www.wikidata.org/wiki/Q2724244","display_name":"Social web","level":3,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2026.3666727","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2026.3666727","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.4073180556297302,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G2185374741","display_name":null,"funder_award_id":"62472348","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8126848880","display_name":null,"funder_award_id":"2024GX-YBXM-533","funder_id":"https://openalex.org/F4320336350","funder_display_name":"Key Research and Development Projects of Shaanxi Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336350","display_name":"Key Research and Development Projects of Shaanxi Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"widespread":[1],"dissemination":[2],"and":[3,20,35,60,122,190,208,238],"misleading":[4],"impact":[5],"of":[6,114,174,223,241],"fake":[7,24,43,224],"news":[8,25,44,69,115,225],"on":[9,230],"the":[10,18,21,75,80,111,124,147,171,221,236],"web":[11],"have":[12,47,71],"become":[13],"a":[14,88,140,153,201,214],"significant":[15],"concern":[16],"for":[17,28,52],"public":[19,232],"government.":[22],"Discovering":[23],"is":[26],"crucial":[27],"ensuring":[29],"that":[30],"users":[31],"receive":[32],"authentic":[33],"information":[34,55,113,193],"maintaining":[36],"social":[37],"harmony.":[38],"However,":[39],"most":[40],"existing":[41],"entity-based":[42],"detection":[45,245],"methods":[46,51],"two":[48],"issues:":[49],"i)":[50],"acquiring":[53],"additional":[54],"through":[56],"entities":[57,66],"lack":[58],"flexibility":[59],"real-time":[61],"capabilities.":[62],"ii)":[63],"approaches":[64],"using":[65,130],"to":[67,145,165,187,204],"capture":[68,146],"semantics":[70],"not":[72],"adequately":[73],"revealed":[74],"interactions":[76,149],"between":[77,150],"words":[78],"in":[79],"text.":[81],"To":[82],"address":[83],"these":[84,127],"issues,":[85],"we":[86,138,180,199],"propose":[87],"<underline":[89,94,97,102],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[90,92,95,98,103,106],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><b>M</b></u>ulti-<underline":[91],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><b>g</b></u>raph":[93],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><b>S</b></u>emantic-aware":[96],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><b>A</b></u>daptive":[99],"Graph":[100],"Convolutional":[101],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><b>N</b></u>etwork":[104],"(<bold":[105],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">MgSAN</b>),":[107],"which":[108],"comprehensively":[109],"captures":[110],"semantic":[112,120,142,156,168,211,217],"texts":[116],"by":[117],"constructing":[118],"multiple":[119],"graphs":[121],"learns":[123],"features":[125],"from":[126,194],"graph":[128,133,144,164,184,196],"structures":[129],"an":[131,161,182],"adaptive":[132,183],"convolutional":[134,185],"network":[135,186],"(SwiGCN).":[136],"Specifically,":[137],"design":[139],"global":[141,207],"interaction":[143],"complex":[148],"words,":[151],"generating":[152],"comprehensive":[154],"textual":[155,176],"representation.":[157],"We":[158],"also":[159],"employ":[160],"entity-noun":[162],"relationship":[163],"mine":[166],"deep":[167,177],"associations,":[169],"enhancing":[170],"model's":[172],"understanding":[173],"fine-grained":[175,210],"meanings.":[178],"Additionally,":[179],"develop":[181],"effectively":[188],"extract":[189],"aggregate":[191],"feature":[192],"different":[195],"structures.":[197],"Finally,":[198],"introduce":[200],"fusion":[202],"module":[203],"integrate":[205],"both":[206],"local":[209],"information,":[212],"forming":[213],"rich":[215],"composite":[216],"representation,":[218],"thereby":[219],"improving":[220],"effectiveness":[222,237],"detection.":[226],"Extensive":[227],"experimental":[228],"results":[229],"three":[231],"benchmark":[233],"datasets":[234],"verify":[235],"superior":[239],"performance":[240],"MgSAN,":[242],"outperforming":[243],"state-of-the-art":[244],"models.":[246]},"counts_by_year":[],"updated_date":"2026-04-11T06:13:24.991567","created_date":"2026-02-21T00:00:00"}
