{"id":"https://openalex.org/W7152993694","doi":"https://doi.org/10.48550/arxiv.2604.08131","title":"Graph Neural Networks for Misinformation Detection: Performance-Efficiency Trade-offs","display_name":"Graph Neural Networks for Misinformation Detection: Performance-Efficiency Trade-offs","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7152993694","doi":"https://doi.org/10.48550/arxiv.2604.08131"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.08131","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08131","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.08131","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017905749","display_name":"Soveatin Kuntur","orcid":"https://orcid.org/0000-0001-6029-941X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kuntur, Soveatin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045096489","display_name":"Maciej Krzywda","orcid":"https://orcid.org/0000-0002-0758-8498"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krzywda, Maciej","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021096406","display_name":"Anna Wr\u00f3blewska","orcid":"https://orcid.org/0000-0003-3728-1216"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wr\u00f3blewska, Anna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053781299","display_name":"Marcin Paprzycki","orcid":"https://orcid.org/0000-0002-8069-2152"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paprzycki, Marcin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017025137","display_name":"Maria Ganzha","orcid":"https://orcid.org/0000-0001-7714-4844"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ganzha, Maria","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103223404","display_name":"Szymon \u0141ukasik","orcid":"https://orcid.org/0000-0002-5716-1476"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u0141ukasik, Szymon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5039341855","display_name":"Amir H. Gandomi","orcid":"https://orcid.org/0000-0002-2798-0104"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gandomi, Amir H.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.9355999827384949,"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":0.9355999827384949,"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":0.014700000174343586,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.0066999997943639755,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6559000015258789},{"id":"https://openalex.org/keywords/misinformation","display_name":"Misinformation","score":0.6308000087738037},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5157999992370605},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44929999113082886},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.414900004863739},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.40389999747276306},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4018000066280365},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.39149999618530273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7953000068664551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6812000274658203},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6559000015258789},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6552000045776367},{"id":"https://openalex.org/C2776990098","wikidata":"https://www.wikidata.org/wiki/Q13579947","display_name":"Misinformation","level":2,"score":0.6308000087738037},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5157999992370605},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44929999113082886},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.414900004863739},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.40389999747276306},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4018000066280365},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.39149999618530273},{"id":"https://openalex.org/C2777548347","wikidata":"https://www.wikidata.org/wiki/Q5456937","display_name":"Flagging","level":2,"score":0.3716000020503998},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.2955000102519989},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.295199990272522},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.26260000467300415},{"id":"https://openalex.org/C2776552730","wikidata":"https://www.wikidata.org/wiki/Q189656","display_name":"Disinformation","level":3,"score":0.25949999690055847},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.08131","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08131","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.08131","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08131","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"rapid":[1],"spread":[2],"of":[3,88],"online":[4],"misinformation":[5,177],"has":[6],"led":[7],"to":[8,84,101,125],"increasingly":[9,173],"complex":[10,174],"detection":[11],"models,":[12],"including":[13],"large":[14],"language":[15],"models":[16,79],"and":[17,24,48,66,76,120,127,167],"hybrid":[18],"architectures.":[19],"However,":[20],"their":[21],"computational":[22],"cost":[23],"deployment":[25],"limitations":[26],"raise":[27],"concerns":[28],"about":[29],"practical":[30],"applicability.":[31],"In":[32],"this":[33],"work,":[34],"we":[35],"benchmark":[36],"graph":[37],"neural":[38],"networks":[39],"(GNNs)":[40],"against":[41,60],"non-graph-based":[42],"machine":[43],"learning":[44],"methods":[45],"under":[46],"controlled":[47],"comparable":[49,153],"conditions.":[50],"We":[51],"evaluate":[52],"lightweight":[53],"GNN":[54],"architectures":[55,175],"(GCN,":[56],"GraphSAGE,":[57],"GAT,":[58],"ChebNet)":[59],"Logistic":[61],"Regression,":[62],"Support":[63],"Vector":[64],"Machines,":[65],"Multilayer":[67],"Perceptrons":[68],"across":[69,109],"seven":[70],"public":[71],"datasets":[72],"in":[73,176],"English,":[74],"Indonesian,":[75],"Polish.":[77],"All":[78],"use":[80],"identical":[81],"TF-IDF":[82],"features":[83],"isolate":[85],"the":[86,159,170],"impact":[87],"relational":[89],"structure.":[90],"Performance":[91],"is":[92],"measured":[93],"using":[94],"F1":[95,117,137],"score,":[96],"with":[97,152],"inference":[98,156],"time":[99],"reported":[100],"assess":[102],"efficiency.":[103],"GNNs":[104,164],"consistently":[105],"outperform":[106],"non-graph":[107],"baselines":[108],"all":[110],"datasets.":[111],"For":[112],"example,":[113],"GraphSAGE":[114,134],"achieves":[115],"96.8%":[116],"on":[118,122,146],"Kaggle":[119],"91.9%":[121],"WELFake,":[123],"compared":[124],"73.2%":[126],"66.8%":[128],"for":[129,172],"MLP,":[130],"respectively.":[131],"On":[132],"COVID-19,":[133],"reaches":[135],"90.5%":[136],"vs.":[138,144],"74.9%,":[139],"while":[140],"ChebNet":[141],"attains":[142],"79.1%":[143],"66.4%":[145],"FakeNewsNet.":[147],"These":[148],"gains":[149],"are":[150],"achieved":[151],"or":[154],"lower":[155],"times.":[157],"Overall,":[158],"results":[160],"show":[161],"that":[162],"classic":[163],"remain":[165],"effective":[166],"efficient,":[168],"challenging":[169],"need":[171],"detection.":[178]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-11T00:00:00"}
