{"id":"https://openalex.org/W3041595026","doi":"https://doi.org/10.1145/3404663.3404680","title":"Semi-Supervised Graph Neural Network with Probabilistic Modeling to Mitigate Uncertainty","display_name":"Semi-Supervised Graph Neural Network with Probabilistic Modeling to Mitigate Uncertainty","publication_year":2020,"publication_date":"2020-05-15","ids":{"openalex":"https://openalex.org/W3041595026","doi":"https://doi.org/10.1145/3404663.3404680","mag":"3041595026"},"language":"en","primary_location":{"id":"doi:10.1145/3404663.3404680","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3404663.3404680","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 the 4th International Conference on Information System and Data Mining","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/A5039675211","display_name":"Maria Vaida","orcid":"https://orcid.org/0000-0002-7869-1900"},"institutions":[{"id":"https://openalex.org/I153151563","display_name":"Harrisburg University of Science and Technology","ror":"https://ror.org/02g0s4z48","country_code":"US","type":"education","lineage":["https://openalex.org/I153151563"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Maria Vaida","raw_affiliation_strings":["Harrisburg University of Science and Technology, Harrisburg, Pennsylvania"],"affiliations":[{"raw_affiliation_string":"Harrisburg University of Science and Technology, Harrisburg, Pennsylvania","institution_ids":["https://openalex.org/I153151563"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001728598","display_name":"Pranita Patil","orcid":"https://orcid.org/0000-0002-3679-3685"},"institutions":[{"id":"https://openalex.org/I153151563","display_name":"Harrisburg University of Science and Technology","ror":"https://ror.org/02g0s4z48","country_code":"US","type":"education","lineage":["https://openalex.org/I153151563"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pranita Patil","raw_affiliation_strings":["Harrisburg University of Science and Technology, Harrisburg, Pennsylvania"],"affiliations":[{"raw_affiliation_string":"Harrisburg University of Science and Technology, Harrisburg, Pennsylvania","institution_ids":["https://openalex.org/I153151563"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5039675211"],"corresponding_institution_ids":["https://openalex.org/I153151563"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.53295435,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"152","last_page":"156"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","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/T11273","display_name":"Advanced Graph Neural Networks","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/T10028","display_name":"Topic Modeling","score":0.9909999966621399,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9872000217437744,"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/computer-science","display_name":"Computer science","score":0.6680760383605957},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6124169826507568},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6111390590667725},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5943295955657959},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.4898824095726013},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4878416061401367},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4831963777542114},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.42584794759750366},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4207279086112976},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.41056379675865173},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3820302188396454},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.26967954635620117},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20484676957130432}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6680760383605957},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6124169826507568},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6111390590667725},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5943295955657959},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.4898824095726013},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4878416061401367},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4831963777542114},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.42584794759750366},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4207279086112976},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.41056379675865173},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3820302188396454},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26967954635620117},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20484676957130432},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3404663.3404680","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3404663.3404680","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 the 4th International Conference on Information System and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","score":0.6299999952316284,"display_name":"No poverty"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1529533208","https://openalex.org/W1533861849","https://openalex.org/W2039688703","https://openalex.org/W2158787690","https://openalex.org/W2315403234","https://openalex.org/W2407873558","https://openalex.org/W2559655401","https://openalex.org/W2595697910","https://openalex.org/W2612690371","https://openalex.org/W2624431344","https://openalex.org/W2803881248","https://openalex.org/W2811124557","https://openalex.org/W2950887295","https://openalex.org/W2963456618","https://openalex.org/W2979683452","https://openalex.org/W2999905431","https://openalex.org/W6660529993","https://openalex.org/W6675319365","https://openalex.org/W6720006811"],"related_works":["https://openalex.org/W2151689585","https://openalex.org/W2380816257","https://openalex.org/W3087071515","https://openalex.org/W4283726152","https://openalex.org/W1525770572","https://openalex.org/W3162204513","https://openalex.org/W1485888979","https://openalex.org/W3172507773","https://openalex.org/W2806680938","https://openalex.org/W4302285290"],"abstract_inverted_index":{"Probabilistic":[0],"Graph":[1,30],"Neural":[2],"Network":[3],"(PGNN)":[4],"introduced":[5],"here":[6],"proposes":[7],"a":[8,92],"machine":[9],"learning":[10,15,68],"application":[11],"that":[12],"combines":[13],"deep":[14,67],"on":[16,52,158],"graphs":[17],"with":[18,69,171],"probabilistic":[19],"modeling,":[20,71],"to":[21,59,74,86,149],"account":[22],"for":[23],"uncertainty":[24,105],"in":[25,39,107,134],"both":[26],"inputs":[27],"and":[28,62,79,97,103,110,124,156,165,174],"outputs.":[29],"neural":[31],"network":[32,95,108],"models":[33,47,133],"(GNNs)":[34],"have":[35],"been":[36],"extensively":[37],"used":[38],"modeling":[40],"graph-structured":[41],"data.":[42],"However":[43],"this":[44,88],"class":[45],"of":[46,122,132,140,152],"learn":[48],"point":[49],"estimates":[50],"conditioned":[51],"arbitrary":[53],"weights":[54,96,123],"initializations,":[55],"which":[56],"can":[57],"lead":[58],"poor":[60],"convergence":[61],"sub-optimal":[63],"generalization.":[64],"By":[65],"enhancing":[66],"Bayesian":[70,176],"PGNN":[72,90,127,146],"aims":[73],"strengthen":[75],"the":[76,101,144,150],"prediction":[77],"accuracy,":[78,164],"provide":[80],"more":[81,166],"stable":[82,167],"predictions.":[83,112,145],"In":[84],"order":[85],"achieve":[87],"goal,":[89],"learns":[91],"distribution":[93],"over":[94,143],"encodings,":[98],"thus":[99],"solving":[100],"epistemic":[102],"aleatoric":[104],"inherited":[106],"parameters":[109],"model":[111],"Through":[113],"Markov":[114],"Chain":[115],"Monte":[116],"Carlo":[117],"Random":[118],"Walk":[119],"Metropolis":[120],"sampling":[121],"likelihood":[125],"distributions,":[126],"essentially":[128],"generates":[129],"an":[130,138],"ensemble":[131],"one":[135],"iteration,":[136],"computing":[137],"estimate":[139],"credible":[141],"intervals":[142],"is":[147],"applied":[148],"task":[151],"semi-supervised":[153],"node":[154],"classification":[155],"tested":[157],"3":[159],"benchmark":[160],"datasets,":[161],"yielding":[162],"improved":[163],"predictions":[168],"when":[169],"compared":[170],"standard":[172],"GNNs":[173],"other":[175],"graph":[177],"models.":[178]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
