{"id":"https://openalex.org/W4410062283","doi":"https://doi.org/10.1007/s10462-025-11231-9","title":"Refined causal graph structure learning via curvature for brain disease classification","display_name":"Refined causal graph structure learning via curvature for brain disease classification","publication_year":2025,"publication_date":"2025-05-03","ids":{"openalex":"https://openalex.org/W4410062283","doi":"https://doi.org/10.1007/s10462-025-11231-9"},"language":"en","primary_location":{"id":"doi:10.1007/s10462-025-11231-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10462-025-11231-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10462-025-11231-9.pdf","source":{"id":"https://openalex.org/S122814990","display_name":"Artificial Intelligence Review","issn_l":"0269-2821","issn":["0269-2821","1573-7462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Review","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10462-025-11231-9.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068971764","display_name":"Falih Gozi Febrinanto","orcid":"https://orcid.org/0000-0002-7932-4571"},"institutions":[{"id":"https://openalex.org/I149672521","display_name":"Federation University","ror":"https://ror.org/05qbzwv83","country_code":"AU","type":"education","lineage":["https://openalex.org/I149672521"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Falih Gozi Febrinanto","raw_affiliation_strings":["Institute of Innovation, Science, and Sustainability, Federation University Australia, Ballarat, VIC, 3353, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Innovation, Science, and Sustainability, Federation University Australia, Ballarat, VIC, 3353, Australia","institution_ids":["https://openalex.org/I149672521"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117425210","display_name":"Adonia Simango","orcid":null},"institutions":[{"id":"https://openalex.org/I4210140438","display_name":"African Explosives (South Africa)","ror":"https://ror.org/03pr1st25","country_code":"ZA","type":"company","lineage":["https://openalex.org/I4210140438"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Adonia Simango","raw_affiliation_strings":["Stainaz Research, Johannesburg, 2192, Gauteng, South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stainaz Research, Johannesburg, 2192, Gauteng, South Africa","institution_ids":["https://openalex.org/I4210140438"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002596064","display_name":"Chengpei Xu","orcid":"https://orcid.org/0000-0001-8860-7701"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Chengpei Xu","raw_affiliation_strings":["MIoT & IPIN Lab, University of New South Wales, Sydney, NSW, 2052, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIoT & IPIN Lab, University of New South Wales, Sydney, NSW, 2052, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060341162","display_name":"Jingjing Zhou","orcid":"https://orcid.org/0000-0003-3373-6164"},"institutions":[{"id":"https://openalex.org/I75059550","display_name":"Zhejiang Gongshang University","ror":"https://ror.org/0569mkk41","country_code":"CN","type":"education","lineage":["https://openalex.org/I75059550"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingjing Zhou","raw_affiliation_strings":["School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, 310018, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, 310018, China","institution_ids":["https://openalex.org/I75059550"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101477958","display_name":"Jiangang Ma","orcid":"https://orcid.org/0000-0002-8449-7610"},"institutions":[{"id":"https://openalex.org/I149672521","display_name":"Federation University","ror":"https://ror.org/05qbzwv83","country_code":"AU","type":"education","lineage":["https://openalex.org/I149672521"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jiangang Ma","raw_affiliation_strings":["Institute of Innovation, Science, and Sustainability, Federation University Australia, Ballarat, VIC, 3353, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Innovation, Science, and Sustainability, Federation University Australia, Ballarat, VIC, 3353, Australia","institution_ids":["https://openalex.org/I149672521"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026685311","display_name":"Sonika Tyagi","orcid":"https://orcid.org/0000-0003-0181-6258"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sonika Tyagi","raw_affiliation_strings":["School of Computing Technologies, RMIT University, Melbourne, VIC, 3000, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing Technologies, RMIT University, Melbourne, VIC, 3000, Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"last","author":{"id":null,"display_name":"Feng Xia","orcid":null},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Feng Xia","raw_affiliation_strings":["School of Computing Technologies, RMIT University, Melbourne, VIC, 3000, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing Technologies, RMIT University, Melbourne, VIC, 3000, Australia","institution_ids":["https://openalex.org/I82951845"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5060341162"],"corresponding_institution_ids":["https://openalex.org/I75059550","https://openalex.org/I82951845"],"apc_list":{"value":3090,"currency":"USD","value_usd":3090},"apc_paid":{"value":3090,"currency":"USD","value_usd":3090},"fwci":0.7577,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.68546101,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"58","issue":"8","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T14393","display_name":"Health, Environment, Cognitive Aging","score":0.9818000197410583,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.9817000031471252,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6852173805236816},{"id":"https://openalex.org/keywords/causal-structure","display_name":"Causal structure","score":0.595525860786438},{"id":"https://openalex.org/keywords/curvature","display_name":"Curvature","score":0.5417343974113464},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5355525016784668},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.42772117257118225},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3873182237148285},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3600035309791565},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.19582334160804749},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14282292127609253},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08710300922393799},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.06530892848968506}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6852173805236816},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.595525860786438},{"id":"https://openalex.org/C195065555","wikidata":"https://www.wikidata.org/wiki/Q214881","display_name":"Curvature","level":2,"score":0.5417343974113464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5355525016784668},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.42772117257118225},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3873182237148285},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3600035309791565},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.19582334160804749},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14282292127609253},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08710300922393799},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.06530892848968506},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10462-025-11231-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10462-025-11231-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10462-025-11231-9.pdf","source":{"id":"https://openalex.org/S122814990","display_name":"Artificial Intelligence Review","issn_l":"0269-2821","issn":["0269-2821","1573-7462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Review","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2506.15708","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.15708","pdf_url":"https://arxiv.org/pdf/2506.15708","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1007/s10462-025-11231-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10462-025-11231-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10462-025-11231-9.pdf","source":{"id":"https://openalex.org/S122814990","display_name":"Artificial Intelligence Review","issn_l":"0269-2821","issn":["0269-2821","1573-7462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Review","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410062283.pdf","grobid_xml":"https://content.openalex.org/works/W4410062283.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1973158444","https://openalex.org/W1976623182","https://openalex.org/W2041782669","https://openalex.org/W2122457251","https://openalex.org/W2167868121","https://openalex.org/W2178225550","https://openalex.org/W2526511911","https://openalex.org/W3005922524","https://openalex.org/W3017445052","https://openalex.org/W3033415292","https://openalex.org/W3138839332","https://openalex.org/W3157697939","https://openalex.org/W3157999218","https://openalex.org/W3199008037","https://openalex.org/W4214698730","https://openalex.org/W4226474153","https://openalex.org/W4296217498","https://openalex.org/W4307847583","https://openalex.org/W4319054544","https://openalex.org/W4353100102","https://openalex.org/W4386046268","https://openalex.org/W4388248823","https://openalex.org/W4390971258","https://openalex.org/W4392017795","https://openalex.org/W4402352631","https://openalex.org/W4404646335","https://openalex.org/W6600013530","https://openalex.org/W6600680261"],"related_works":["https://openalex.org/W55883722","https://openalex.org/W2394379345","https://openalex.org/W2079726820","https://openalex.org/W3215763795","https://openalex.org/W4288816678","https://openalex.org/W1988570269","https://openalex.org/W2002924703","https://openalex.org/W2946668990","https://openalex.org/W2380616566","https://openalex.org/W4388585861"],"abstract_inverted_index":{"Abstract":[0],"Graph":[1],"neural":[2],"networks":[3,82],"(GNNs)":[4],"have":[5,20],"been":[6],"developed":[7],"to":[8,50,105,123,129],"model":[9,140],"the":[10,36,85,125],"relationship":[11,38],"between":[12,42,56,99],"regions":[13],"of":[14,30,39],"interest":[15],"(ROIs)":[16],"in":[17,24,151],"brains":[18],"and":[19,53,91,133],"shown":[21],"significant":[22],"improvement":[23],"detecting":[25],"brain":[26,43,75,81,107,155],"diseases.":[27],"However,":[28],"most":[29],"these":[31],"frameworks":[32],"do":[33],"not":[34],"consider":[35],"intrinsic":[37],"causality":[40],"factor":[41],"ROIs,":[44],"which":[45,78],"is":[46],"arguably":[47],"more":[48,131],"essential":[49],"observe":[51],"cause":[52],"effect":[54],"interaction":[55],"signals":[57],"rather":[58],"than":[59],"typical":[60],"correlation":[61],"values.":[62],"We":[63],"propose":[64],"a":[65,115,119],"novel":[66],"framework":[67],"called":[68],"Causal":[69],"Graphs":[70],"for":[71,74],"Brains":[72],"(CGB)":[73],"disease":[76,108,156],"classification/detection,":[77],"models":[79],"refined":[80],"based":[83],"on":[84,154],"causal":[86,97,127],"discovery":[87],"method,":[88],"transfer":[89],"entropy,":[90],"geometric":[92,120],"curvature":[93,121],"strategy.":[94],"CGB":[95,112,147],"unveils":[96],"relationships":[98],"ROIs":[100],"that":[101,146],"bring":[102],"vital":[103],"information":[104,136],"enhance":[106],"classification":[109,152],"performance.":[110],"Furthermore,":[111],"also":[113],"performs":[114],"graph":[116,128],"rewiring":[117],"through":[118],"strategy":[122],"refine":[124],"generated":[126],"become":[130],"expressive":[132],"reduce":[134],"potential":[135],"bottlenecks":[137],"when":[138],"GNNs":[139],"it.":[141],"Our":[142],"extensive":[143],"experiments":[144],"show":[145],"outperforms":[148],"state-of-the-art":[149],"methods":[150],"tasks":[153],"datasets,":[157],"as":[158],"measured":[159],"by":[160],"average":[161],"F1":[162],"scores.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}