{"id":"https://openalex.org/W4413051433","doi":"https://doi.org/10.1109/tpami.2025.3596918","title":"Cross-Spectral Analysis of Bivariate Graph Signals","display_name":"Cross-Spectral Analysis of Bivariate Graph Signals","publication_year":2025,"publication_date":"2025-08-07","ids":{"openalex":"https://openalex.org/W4413051433","doi":"https://doi.org/10.1109/tpami.2025.3596918","pmid":"https://pubmed.ncbi.nlm.nih.gov/40773388"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2025.3596918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2025.3596918","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5002536452","display_name":"Kyusoon Kim","orcid":"https://orcid.org/0000-0002-9035-1474"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]},{"id":"https://openalex.org/I141371507","display_name":"Soongsil University","ror":"https://ror.org/017xnm587","country_code":"KR","type":"education","lineage":["https://openalex.org/I141371507"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Kyusoon Kim","raw_affiliation_strings":["Department of Statistics and Actuarial Science, Soongsil University, Seoul, South Korea","Department of Statistics, Seoul National University, Seoul, Korea"],"raw_orcid":"https://orcid.org/0000-0002-9035-1474","affiliations":[{"raw_affiliation_string":"Department of Statistics and Actuarial Science, Soongsil University, Seoul, South Korea","institution_ids":["https://openalex.org/I141371507"]},{"raw_affiliation_string":"Department of Statistics, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032931435","display_name":"Hee\u2010Seok Oh","orcid":"https://orcid.org/0000-0002-1501-0530"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hee-Seok Oh","raw_affiliation_strings":["Department of Statistics, Seoul National University, Seoul, South Korea","Department of Statistics, Seoul National University, Seoul, Korea"],"raw_orcid":"https://orcid.org/0000-0002-1501-0530","affiliations":[{"raw_affiliation_string":"Department of Statistics, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"Department of Statistics, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5002536452"],"corresponding_institution_ids":["https://openalex.org/I139264467","https://openalex.org/I141371507"],"apc_list":null,"apc_paid":null,"fwci":3.927,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.93836212,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"47","issue":"12","first_page":"11141","last_page":"11151"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9842000007629395,"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":0.9842000007629395,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9562000036239624,"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/bivariate-analysis","display_name":"Bivariate analysis","score":0.7782779335975647},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6462768912315369},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6259238719940186},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5968990325927734},{"id":"https://openalex.org/keywords/power-graph-analysis","display_name":"Power graph analysis","score":0.5388407707214355},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.46931788325309753},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.45670104026794434},{"id":"https://openalex.org/keywords/bivariate-data","display_name":"Bivariate data","score":0.43025654554367065},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40829381346702576},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3906762897968292},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23239213228225708},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2027471661567688},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1265774369239807},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12450483441352844}],"concepts":[{"id":"https://openalex.org/C64341305","wikidata":"https://www.wikidata.org/wiki/Q4919225","display_name":"Bivariate analysis","level":2,"score":0.7782779335975647},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6462768912315369},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6259238719940186},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5968990325927734},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.5388407707214355},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.46931788325309753},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.45670104026794434},{"id":"https://openalex.org/C148264743","wikidata":"https://www.wikidata.org/wiki/Q4919224","display_name":"Bivariate data","level":3,"score":0.43025654554367065},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40829381346702576},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3906762897968292},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23239213228225708},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2027471661567688},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1265774369239807},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12450483441352844}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2025.3596918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2025.3596918","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:40773388","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40773388","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W356566955","https://openalex.org/W391578156","https://openalex.org/W1540550726","https://openalex.org/W1698699930","https://openalex.org/W1968709967","https://openalex.org/W1971368490","https://openalex.org/W1978601068","https://openalex.org/W1991252559","https://openalex.org/W2004559848","https://openalex.org/W2016423476","https://openalex.org/W2045003357","https://openalex.org/W2046033161","https://openalex.org/W2046275336","https://openalex.org/W2106822551","https://openalex.org/W2112683316","https://openalex.org/W2158787690","https://openalex.org/W2161763921","https://openalex.org/W2235146554","https://openalex.org/W2299462150","https://openalex.org/W2343344684","https://openalex.org/W2518878983","https://openalex.org/W2626271247","https://openalex.org/W2632499194","https://openalex.org/W2763569390","https://openalex.org/W2766445337","https://openalex.org/W2776807095","https://openalex.org/W2884534819","https://openalex.org/W2966253772","https://openalex.org/W2969273637","https://openalex.org/W2995875879","https://openalex.org/W3032119513","https://openalex.org/W3101413764","https://openalex.org/W3111983220","https://openalex.org/W3183705489","https://openalex.org/W3199986655","https://openalex.org/W4224020312","https://openalex.org/W4225871914","https://openalex.org/W4237386736","https://openalex.org/W4254782144","https://openalex.org/W4290791717","https://openalex.org/W4293507940","https://openalex.org/W4366263357","https://openalex.org/W4386568572","https://openalex.org/W4389352409","https://openalex.org/W4395096403","https://openalex.org/W4396242587","https://openalex.org/W4399167974","https://openalex.org/W4400309724"],"related_works":["https://openalex.org/W4232399475","https://openalex.org/W2474319518","https://openalex.org/W89977309","https://openalex.org/W1575790908","https://openalex.org/W3122841410","https://openalex.org/W4447228","https://openalex.org/W2901858382","https://openalex.org/W2150332776","https://openalex.org/W2060480178","https://openalex.org/W2027453011"],"abstract_inverted_index":{"With":[0],"the":[1,19,27,36,62,100,105,109,115,118,146],"advancements":[2],"in":[3,145],"technology":[4],"and":[5,25,84,89,103,127],"monitoring":[6],"tools,":[7],"we":[8,43,77,113,137],"often":[9],"encounter":[10],"multivariate":[11,22],"graph":[12,23,51,68,72,82,86,93,143],"signals,":[13],"which":[14],"can":[15],"be":[16],"seen":[17],"as":[18,133],"realizations":[20],"of":[21,35,56,64,67,108,117,142,148],"processes,":[24],"revealing":[26],"relationship":[28],"between":[29],"their":[30],"constituent":[31],"quantities":[32],"is":[33,59],"one":[34],"important":[37],"problems.":[38],"To":[39],"address":[40],"this":[41,57,75],"issue,":[42],"propose":[44,96],"a":[45,128],"cross-spectral":[46,87,101],"analysis":[47,66,141],"tool":[48],"for":[49,91,99],"bivariate":[50,71,92],"signals.":[52,73],"The":[53],"main":[54],"goal":[55],"study":[58],"to":[60,70],"extend":[61],"scope":[63],"spectral":[65,140],"signals":[69,144],"In":[74],"study,":[76],"define":[78],"joint":[79],"weak":[80],"stationarity":[81],"processes":[83],"introduce":[85],"density":[88,102],"coherence":[90],"processes.":[94],"We":[95],"several":[97],"estimators":[98,120],"investigate":[104],"theoretical":[106],"properties":[107],"proposed":[110,119],"estimators.":[111],"Furthermore,":[112],"demonstrate":[114],"effectiveness":[116],"through":[121],"numerical":[122],"experiments,":[123],"including":[124],"simulation":[125],"studies":[126],"real":[129],"data":[130],"application.":[131],"Finally,":[132],"an":[134],"interesting":[135],"extension,":[136],"discuss":[138],"robust":[139],"presence":[147],"outliers.":[149]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-18T08:16:58.900851","created_date":"2025-10-10T00:00:00"}
