{"id":"https://openalex.org/W4416252329","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227576","title":"Graphs to the Rescue: Revealing Hidden Relationships in Survey Data","display_name":"Graphs to the Rescue: Revealing Hidden Relationships in Survey Data","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416252329","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227576"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227576","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227576","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-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/A5049369263","display_name":"Bruno Messias Farias de Resende","orcid":null},"institutions":[{"id":"https://openalex.org/I4210092357","display_name":"Institute of Mathematics and Computer Science","ror":"https://ror.org/00fy4at53","country_code":"MD","type":"facility","lineage":["https://openalex.org/I112090838","https://openalex.org/I4210092357"]}],"countries":["MD"],"is_corresponding":true,"raw_author_name":"Bruno M. F. de Resende","raw_affiliation_strings":["Universidade de São Paulo,Instituto de Física de São Carlos (IFSC),Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade de São Paulo,Instituto de Física de São Carlos (IFSC),Brazil","institution_ids":["https://openalex.org/I4210092357"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084551356","display_name":"Alexandre C. B. Delbem","orcid":"https://orcid.org/0000-0003-1810-1742"},"institutions":[{"id":"https://openalex.org/I4210092357","display_name":"Institute of Mathematics and Computer Science","ror":"https://ror.org/00fy4at53","country_code":"MD","type":"facility","lineage":["https://openalex.org/I112090838","https://openalex.org/I4210092357"]}],"countries":["MD"],"is_corresponding":false,"raw_author_name":"Alexandre C. B. Delbem","raw_affiliation_strings":["Universidade de São Paulo,Instituto de Ciências Matemáticas e de Computação (ICMC),Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade de São Paulo,Instituto de Ciências Matemáticas e de Computação (ICMC),Brazil","institution_ids":["https://openalex.org/I4210092357"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014336297","display_name":"Eric K. Tokuda","orcid":"https://orcid.org/0000-0002-6159-2500"},"institutions":[{"id":"https://openalex.org/I4210092357","display_name":"Institute of Mathematics and Computer Science","ror":"https://ror.org/00fy4at53","country_code":"MD","type":"facility","lineage":["https://openalex.org/I112090838","https://openalex.org/I4210092357"]}],"countries":["MD"],"is_corresponding":false,"raw_author_name":"Eric K. Tokuda","raw_affiliation_strings":["Universidade de São Paulo,Instituto de Ciências Matemáticas e de Computação (ICMC),Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade de São Paulo,Instituto de Ciências Matemáticas e de Computação (ICMC),Brazil","institution_ids":["https://openalex.org/I4210092357"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5049369263"],"corresponding_institution_ids":["https://openalex.org/I4210092357"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19527565,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.2387000024318695,"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":0.2387000024318695,"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/T14393","display_name":"Health, Environment, Cognitive Aging","score":0.13619999587535858,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.055799998342990875,"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/feature","display_name":"Feature (linguistics)","score":0.6122000217437744},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.5859000086784363},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5430999994277954},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.5156999826431274},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4945000112056732},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4059999883174896},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.3711000084877014}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7099999785423279},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6122000217437744},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5859000086784363},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5430999994277954},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.5156999826431274},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4945000112056732},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45190000534057617},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.44179999828338623},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.430400013923645},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4059999883174896},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.362199991941452},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227576","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227576","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1928856156","https://openalex.org/W1974071652","https://openalex.org/W2026907796","https://openalex.org/W2038793346","https://openalex.org/W2065528503","https://openalex.org/W2067003450","https://openalex.org/W2095293504","https://openalex.org/W2100495367","https://openalex.org/W2111642621","https://openalex.org/W2116341502","https://openalex.org/W2140000690","https://openalex.org/W2171356466","https://openalex.org/W2460760569","https://openalex.org/W2531871281","https://openalex.org/W2962756421","https://openalex.org/W2996613385","https://openalex.org/W3004028296","https://openalex.org/W3011393870","https://openalex.org/W3045647162","https://openalex.org/W3128426437","https://openalex.org/W4235019172","https://openalex.org/W4256330813","https://openalex.org/W4298082496","https://openalex.org/W4402843978"],"related_works":[],"abstract_inverted_index":{"Extracting":[0],"meaningful":[1,164],"insights":[2],"from":[3],"tabular":[4,34,48,116,189],"data":[5,49],"remains":[6],"a":[7,28,51,69,103,134,183],"fundamental":[8],"challenge":[9],"in":[10,123,194],"machine":[11],"learning,":[12],"as":[13,50,198],"traditional":[14],"methods":[15],"often":[16],"struggle":[17],"to":[18,109,119,143,160],"capture":[19],"complex":[20,188],"feature":[21,58,124,149],"interactions.":[22],"In":[23],"this":[24],"work,":[25],"we":[26,67,101],"propose":[27],"novel":[29],"graph-based":[30],"approach":[31,181],"for":[32,186],"analyzing":[33],"datasets":[35],"by":[36,177],"leveraging":[37],"spectral":[38,154],"graph":[39,88],"theory":[40],"and":[41,114,147,169,201],"community":[42],"detection":[43],"techniques.":[44,179],"Our":[45,126],"method":[46],"represents":[47],"weighted":[52],"directed":[53],"graph,":[54],"where":[55],"edges":[56],"encode":[57],"dependencies":[59,97],"based":[60],"on":[61,130,137],"SHAP":[62],"values.":[63],"To":[64],"enhance":[65],"interpretability,":[66],"apply":[68],"sparsification":[70],"technique":[71],"that":[72,153,173],"retains":[73],"only":[74],"the":[75,82,86,90,131],"most":[76],"significant":[77],"connections.":[78],"We":[79],"further":[80],"analyze":[81],"structural":[83],"properties":[84],"of":[85],"resulting":[87],"using":[89],"deformed":[91],"magnetic":[92],"Laplacian,":[93],"which":[94],"captures":[95],"directional":[96],"among":[98],"features.":[99],"Additionally,":[100],"employ":[102],"nonparametric":[104],"stochastic":[105],"block":[106],"model":[107],"(nSBM)":[108],"uncover":[110],"hierarchical":[111],"modular":[112],"structures":[113,146],"use":[115],"embeddings":[117],"(tab2vec)":[118],"reveal":[120,144],"fine-grained":[121],"relationships":[122,172],"space.":[125],"framework":[127],"is":[128],"validated":[129],"PeNSE":[132],"dataset,":[133],"large-scale":[135],"survey":[136],"adolescent":[138],"health,":[139],"demonstrating":[140],"its":[141],"ability":[142],"hidden":[145],"improve":[148],"interpretability.":[150],"Results":[151],"show":[152],"analysis":[155],"provides":[156],"an":[157],"effective":[158],"way":[159],"categorize":[161],"features":[162],"into":[163],"clusters,":[165],"identify":[166],"redundant":[167],"variables,":[168],"highlight":[170],"key":[171],"may":[174],"be":[175],"overlooked":[176],"conventional":[178],"This":[180],"offers":[182],"powerful":[184],"alternative":[185],"exploring":[187],"datasets,":[190],"with":[191],"potential":[192],"applications":[193],"various":[195],"domains":[196],"such":[197],"healthcare,":[199],"finance,":[200],"social":[202],"sciences.":[203]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}