{"id":"https://openalex.org/W7138268636","doi":"https://doi.org/10.1109/access.2026.3674781","title":"Static-Graph Estimation in Hybrid GNNs Using Behavioral-Similarity and Clustering-Based Methods","display_name":"Static-Graph Estimation in Hybrid GNNs Using Behavioral-Similarity and Clustering-Based Methods","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7138268636","doi":"https://doi.org/10.1109/access.2026.3674781"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3674781","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3674781","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.2026.3674781","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125697657","display_name":"Ryusei Otani","orcid":null},"institutions":[{"id":"https://openalex.org/I197274945","display_name":"Nagoya Institute of Technology","ror":"https://ror.org/055yf1005","country_code":"JP","type":"education","lineage":["https://openalex.org/I197274945"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ryusei Otani","raw_affiliation_strings":["Department of Computer Science, Nagoya Institute of Technology, Nagoya, Aichi, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Nagoya Institute of Technology, Nagoya, Aichi, Japan","institution_ids":["https://openalex.org/I197274945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063558614","display_name":"Keiichi Namikoshi","orcid":null},"institutions":[{"id":"https://openalex.org/I197274945","display_name":"Nagoya Institute of Technology","ror":"https://ror.org/055yf1005","country_code":"JP","type":"education","lineage":["https://openalex.org/I197274945"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Keiichi Namikoshi","raw_affiliation_strings":["Department of Computer Science, Nagoya Institute of Technology, Nagoya, Aichi, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Nagoya Institute of Technology, Nagoya, Aichi, Japan","institution_ids":["https://openalex.org/I197274945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000871669","display_name":"Y. Sakurai","orcid":"https://orcid.org/0000-0001-6389-0117"},"institutions":[{"id":"https://openalex.org/I197274945","display_name":"Nagoya Institute of Technology","ror":"https://ror.org/055yf1005","country_code":"JP","type":"education","lineage":["https://openalex.org/I197274945"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuko Sakurai","raw_affiliation_strings":["Department of Computer Science, Nagoya Institute of Technology, Nagoya, Aichi, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Nagoya Institute of Technology, Nagoya, Aichi, Japan","institution_ids":["https://openalex.org/I197274945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019911052","display_name":"Mingyu Guo","orcid":"https://orcid.org/0000-0002-3478-9201"},"institutions":[{"id":"https://openalex.org/I5681781","display_name":"The University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I5681781"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Mingyu Guo","raw_affiliation_strings":["School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, SA, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, SA, Australia","institution_ids":["https://openalex.org/I5681781"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056709028","display_name":"Satoshi Oyama","orcid":"https://orcid.org/0000-0002-8124-3578"},"institutions":[{"id":"https://openalex.org/I33858575","display_name":"Nagoya City University","ror":"https://ror.org/04wn7wc95","country_code":"JP","type":"education","lineage":["https://openalex.org/I33858575"]},{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Satoshi Oyama","raw_affiliation_strings":["Graduate School of Data Science, Nagoya City University, Nagoya, Aichi, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Data Science, Nagoya City University, Nagoya, Aichi, Japan","institution_ids":["https://openalex.org/I33858575","https://openalex.org/I60134161"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5125697657"],"corresponding_institution_ids":["https://openalex.org/I197274945"],"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":0.79404446,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"42734","last_page":"42750"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.8845000267028809,"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":0.8845000267028809,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.023099999874830246,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10320","display_name":"Neural Networks and Applications","score":0.007600000128149986,"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/estimation","display_name":"Estimation","score":0.446399986743927},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.32690000534057617},{"id":"https://openalex.org/keywords/algorithm-design","display_name":"Algorithm design","score":0.2581999897956848},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.2492000013589859}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7074000239372253},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.446399986743927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33469998836517334},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33000001311302185},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32330000400543213},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28380000591278076},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.262800008058548},{"id":"https://openalex.org/C106516650","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm design","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2492000013589859}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3674781","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3674781","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"},{"id":"pmh:oai:doaj.org/article:3aeab28e4c2745e49ec8a79cc4a087bf","is_oa":true,"landing_page_url":"https://doaj.org/article/3aeab28e4c2745e49ec8a79cc4a087bf","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 42734-42750 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3674781","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3674781","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Hybrid":[0],"GNNs,":[1],"which":[2],"learn":[3],"both":[4],"long-term":[5],"structural":[6],"information":[7,74,194],"encoded":[8],"in":[9],"static":[10,31,96,125,169,186],"graphs":[11,105,126,187],"and":[12,117,145,165,211,239],"temporal":[13],"interactions":[14,164],"within":[15,171],"dynamic":[16,104,133,163],"graphs,":[17],"have":[18],"attracted":[19],"attention":[20],"for":[21,188],"their":[22],"high":[23],"predictive":[24,209],"accuracy.":[25],"In":[26],"practical":[27],"applications,":[28],"however,":[29],"the":[30,38,58,66,88,175,182,204,208,226],"relational":[32],"data":[33,237],"required":[34],"as":[35,98,106],"part":[36],"of":[37,90,184,214,228],"input":[39],"is":[40,68,75,195],"not":[41],"always":[42],"available.":[43],"Furthermore,":[44],"many":[45],"existing":[46],"static-graph":[47,111,232],"construction":[48,112,183,220,233],"methods":[49,116,123,206],"rely":[50],"on":[51,128,236],"domain-specific":[52],"prior":[53],"knowledge":[54],"or":[55],"node":[56,138,159],"attributes;":[57],"former":[59],"hinders":[60],"generalization":[61],"to":[62,70,158,218],"other":[63],"domains,":[64],"while":[65],"latter":[67],"difficult":[69],"apply":[71],"when":[72,192],"attribute":[73,193],"scarce.":[76],"To":[77],"address":[78],"these":[79],"issues,":[80],"this":[81],"paper":[82],"proposes":[83],"a":[84,95,99,142,167],"framework":[85],"that":[86,203],"enables":[87],"application":[89,240],"hybrid":[91,189,215],"GNNs":[92,190,216],"by":[93],"estimating":[94],"graph":[97,170],"preprocessing":[100],"step":[101],"using":[102,174,198],"only":[103],"input.":[107],"We":[108],"introduce":[109],"three":[110],"methods:":[113],"two":[114],"co-occurrence-based":[115,122],"one":[118],"clustering-based":[119,153],"method.":[120,178],"The":[121,152,222],"construct":[124],"based":[127,235],"\u2018\u2018behavioral":[129],"similarity\u2019\u2019":[130],"derived":[131,161],"from":[132,162],"interactions.":[134],"Specifically,":[135],"they":[136],"aggregate":[137],"co-occurrence":[139],"frequencies":[140],"over":[141],"certain":[143],"period":[144],"connect":[146],"nodes":[147],"with":[148],"similar":[149],"behavioral":[150],"tendencies.":[151],"method":[154,234],"applies":[155],"K-means":[156],"clustering":[157],"representations":[160],"constructs":[166],"sparse":[168],"each":[172],"cluster":[173],"k-nearest":[176],"neighbor":[177],"These":[179],"approaches":[180],"enable":[181],"effective":[185],"even":[191],"limited.":[196],"Evaluations":[197],"five":[199],"real-world":[200],"datasets":[201],"demonstrated":[202],"proposed":[205],"improve":[207],"accuracy":[210],"training":[212],"efficiency":[213],"compared":[217],"attribute-based":[219],"methods.":[221],"results":[223],"further":[224],"highlight":[225],"importance":[227],"selecting":[229],"an":[230],"appropriate":[231],"characteristics":[238],"scenarios.":[241]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2026-03-18T00:00:00"}