{"id":"https://openalex.org/W4221116764","doi":"https://doi.org/10.1145/3508071","title":"PSL: An Algorithm for Partial Bayesian Network Structure Learning","display_name":"PSL: An Algorithm for Partial Bayesian Network Structure Learning","publication_year":2022,"publication_date":"2022-03-09","ids":{"openalex":"https://openalex.org/W4221116764","doi":"https://doi.org/10.1145/3508071"},"language":"en","primary_location":{"id":"doi:10.1145/3508071","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3508071","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-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/A5088982563","display_name":"Zhaolong Ling","orcid":"https://orcid.org/0000-0003-4812-6676"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaolong Ling","raw_affiliation_strings":["Anhui University, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0003-4812-6676","affiliations":[{"raw_affiliation_string":"Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100719462","display_name":"Kui Yu","orcid":"https://orcid.org/0000-0003-2442-4572"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kui Yu","raw_affiliation_strings":["Hefei University of Technology, Hefei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hefei University of Technology, Hefei, China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100383342","display_name":"Lin Liu","orcid":"https://orcid.org/0000-0003-2843-5738"},"institutions":[{"id":"https://openalex.org/I170239107","display_name":"University of South Australia","ror":"https://ror.org/01p93h210","country_code":"AU","type":"education","lineage":["https://openalex.org/I170239107"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Lin Liu","raw_affiliation_strings":["University of South Australia, Adelaide, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Australia, Adelaide, Australia","institution_ids":["https://openalex.org/I170239107"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012177739","display_name":"Jiuyong Li","orcid":"https://orcid.org/0000-0002-9023-1878"},"institutions":[{"id":"https://openalex.org/I170239107","display_name":"University of South Australia","ror":"https://ror.org/01p93h210","country_code":"AU","type":"education","lineage":["https://openalex.org/I170239107"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jiuyong Li","raw_affiliation_strings":["University of South Australia, Adelaide, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Australia, Adelaide, Australia","institution_ids":["https://openalex.org/I170239107"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100430650","display_name":"Yiwen Zhang","orcid":"https://orcid.org/0000-0001-8709-1088"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiwen Zhang","raw_affiliation_strings":["Anhui University, Hefei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080738591","display_name":"Xindong Wu","orcid":"https://orcid.org/0000-0003-2396-1704"},"institutions":[{"id":"https://openalex.org/I4401726980","display_name":"Mininglamp (China)","ror":"https://ror.org/04tb90x61","country_code":"CN","type":"company","lineage":["https://openalex.org/I4401726980"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xindong Wu","raw_affiliation_strings":["Mininglamp Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mininglamp Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726980"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8988,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.77911981,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"16","issue":"5","first_page":"1","last_page":"25"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9998000264167786,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9998000264167786,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.963699996471405,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10050","display_name":"Multi-Criteria Decision Making","score":0.9358999729156494,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/psl","display_name":"PSL","score":0.7167234420776367},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6672427654266357},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5893060564994812},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5801100134849548},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5361416935920715},{"id":"https://openalex.org/keywords/markov-blanket","display_name":"Markov blanket","score":0.5246667861938477},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5214228630065918},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.49557459354400635},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4721130132675171},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.4104887843132019},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3627229332923889},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.2967808246612549},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2768890857696533},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.25994452834129333},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.09261232614517212},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08433982729911804}],"concepts":[{"id":"https://openalex.org/C10464949","wikidata":"https://www.wikidata.org/wiki/Q1914781","display_name":"PSL","level":2,"score":0.7167234420776367},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6672427654266357},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5893060564994812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5801100134849548},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5361416935920715},{"id":"https://openalex.org/C123867240","wikidata":"https://www.wikidata.org/wiki/Q3001792","display_name":"Markov blanket","level":5,"score":0.5246667861938477},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5214228630065918},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.49557459354400635},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4721130132675171},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.4104887843132019},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3627229332923889},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.2967808246612549},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2768890857696533},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25994452834129333},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.09261232614517212},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08433982729911804},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C189973286","wikidata":"https://www.wikidata.org/wiki/Q176695","display_name":"Markov property","level":4,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3508071","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3508071","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},{"id":"pmh:oai:urm_publish:9916679025501831","is_oa":false,"landing_page_url":"https://find.library.unisa.edu.au/discovery/fulldisplay/alma9916679025501831/61USOUTHAUS_INST:ROR","pdf_url":null,"source":{"id":"https://openalex.org/S4306402528","display_name":"UniSA Research Outputs Repository (University of South Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I170239107","host_organization_name":"University of South Australia","host_organization_lineage":["https://openalex.org/I170239107"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[{"id":"https://openalex.org/G1776606928","display_name":"Fairness aware data mining for discrimination free decision-making","funder_award_id":"DP200101210","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W178169250","https://openalex.org/W1505477995","https://openalex.org/W1530398305","https://openalex.org/W1556316174","https://openalex.org/W1587150650","https://openalex.org/W1977751843","https://openalex.org/W2031779765","https://openalex.org/W2039784851","https://openalex.org/W2099900459","https://openalex.org/W2108232944","https://openalex.org/W2120450109","https://openalex.org/W2128088446","https://openalex.org/W2133091666","https://openalex.org/W2134240743","https://openalex.org/W2156571267","https://openalex.org/W2161205483","https://openalex.org/W2165190832","https://openalex.org/W2169030506","https://openalex.org/W2257550357","https://openalex.org/W2324228145","https://openalex.org/W2525748243","https://openalex.org/W2798149936","https://openalex.org/W2911495555","https://openalex.org/W2925608410","https://openalex.org/W2979638007","https://openalex.org/W2980507899","https://openalex.org/W2998216295","https://openalex.org/W3091533194","https://openalex.org/W3097319355","https://openalex.org/W3120419251","https://openalex.org/W3155575086","https://openalex.org/W4210741731","https://openalex.org/W4236169852","https://openalex.org/W4236354166","https://openalex.org/W4255761846","https://openalex.org/W4302423442","https://openalex.org/W7066667914"],"related_works":["https://openalex.org/W3207148653","https://openalex.org/W2994546694","https://openalex.org/W2790852836","https://openalex.org/W2379348558","https://openalex.org/W2124494398","https://openalex.org/W3023326395","https://openalex.org/W2392423725","https://openalex.org/W23237351","https://openalex.org/W2159351263","https://openalex.org/W2811009994"],"abstract_inverted_index":{"Learning":[0],"partial":[1,140],"Bayesian":[2],"network":[3],"(BN)":[4],"structure":[5,23,33,38,75,142],"is":[6,16,34,155],"an":[7,82],"interesting":[8],"and":[9,78,84,109,121,175],"challenging":[10],"problem.":[11],"In":[12],"this":[13,59],"challenge,":[14],"it":[15,113],"computationally":[17],"expensive":[18],"to":[19,48],"use":[20],"global":[21],"BN":[22,32,37,74,88,141],"learning":[24,39,76],"algorithms,":[25],"while":[26],"only":[27],"one":[28],"part":[29],"of":[30,51,66,119,126,131,150,177],"a":[31,43,63,100],"interesting,":[35],"local":[36,73],"algorithms":[40,77],"are":[41,143],"not":[42],"favourable":[44],"solution":[45],"either":[46],"due":[47],"the":[49,57,67,116,129,132,139,148,152,173,178],"issue":[50,71],"false":[52,68],"edge":[53,69],"orientation.":[54],"To":[55,145],"address":[56],"problem,":[58],"article":[60],"first":[61],"presents":[62],"detailed":[64],"analysis":[65],"orientation":[70],"with":[72,168],"then":[79,112],"proposes":[80],"PSL,":[81,151],"efficient":[83],"accurate":[85],"P":[86],"artial":[87],"S":[89,161],"tructure":[90],"L":[91],"earning":[92],"(PSL)":[93],"algorithm.":[94],"Specifically,":[95],"PSL":[96],"divides":[97],"V-structures":[98,108,127],"in":[99,128,138],"Markov":[101],"blanket":[102],"(MB)":[103],"into":[104,164],"two":[105],"types:":[106],"Type-C":[107],"Type-NC":[110],"V-structures,":[111],"starts":[114],"from":[115],"given":[117],"node":[118,134],"interest":[120],"recursively":[122],"finds":[123],"both":[124],"types":[125],"MB":[130],"current":[133],"until":[135],"all":[136],"edges":[137],"oriented.":[144],"further":[146],"improve":[147],"efficiency":[149,174],"PSL-FS":[153],"algorithm":[154],"designed":[156],"by":[157],"incorporating":[158],"F":[159],"eature":[160],"election":[162],"(FS)":[163],"PSL.":[165],"Extensive":[166],"experiments":[167],"six":[169],"benchmark":[170],"BNs":[171],"validate":[172],"accuracy":[176],"proposed":[179],"algorithms.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2022-04-03T00:00:00"}
