{"id":"https://openalex.org/W3027594385","doi":"https://doi.org/10.3390/make2020008","title":"Exploiting Weak Ties in Incomplete Network Datasets Using Simplified Graph Convolutional Neural Networks","display_name":"Exploiting Weak Ties in Incomplete Network Datasets Using Simplified Graph Convolutional Neural Networks","publication_year":2020,"publication_date":"2020-05-21","ids":{"openalex":"https://openalex.org/W3027594385","doi":"https://doi.org/10.3390/make2020008","mag":"3027594385"},"language":"en","primary_location":{"id":"doi:10.3390/make2020008","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make2020008","pdf_url":"https://www.mdpi.com/2504-4990/2/2/8/pdf?version=1591670153","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/2/2/8/pdf?version=1591670153","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013248060","display_name":"Neda Hajiakhoond Bidoki","orcid":"https://orcid.org/0000-0002-6741-6735"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Neda H. Bidoki","raw_affiliation_strings":["Department of Computer Science, University of Central Florida (UCF), Orlando, FL 32816, USA"],"raw_orcid":"https://orcid.org/0000-0002-6741-6735","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Central Florida (UCF), Orlando, FL 32816, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058939672","display_name":"Alexander V. Mantzaris","orcid":"https://orcid.org/0000-0002-0026-5725"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander V. Mantzaris","raw_affiliation_strings":["Department of Statistics and Data Science, University of Central Florida (UCF), Orlando, FL 32816, USA"],"raw_orcid":"https://orcid.org/0000-0002-0026-5725","affiliations":[{"raw_affiliation_string":"Department of Statistics and Data Science, University of Central Florida (UCF), Orlando, FL 32816, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025542785","display_name":"Gita Sukthankar","orcid":"https://orcid.org/0000-0002-6863-6609"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Gita Sukthankar","raw_affiliation_strings":["Department of Computer Science, University of Central Florida (UCF), Orlando, FL 32816, USA"],"raw_orcid":"https://orcid.org/0000-0002-6863-6609","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Central Florida (UCF), Orlando, FL 32816, USA","institution_ids":["https://openalex.org/I106165777"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5025542785"],"corresponding_institution_ids":["https://openalex.org/I106165777"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.272,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63263621,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"2","issue":"2","first_page":"125","last_page":"146"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9965000152587891,"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"}},{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9678999781608582,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/centrality","display_name":"Centrality","score":0.7868548631668091},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7401134371757507},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.7026818990707397},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6610085964202881},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5801545977592468},{"id":"https://openalex.org/keywords/interpersonal-ties","display_name":"Interpersonal ties","score":0.486316055059433},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.45996978878974915},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40866848826408386},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3930571377277374},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3783068060874939},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15888166427612305},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07541459798812866},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.061332106590270996},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.05954867601394653}],"concepts":[{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.7868548631668091},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7401134371757507},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.7026818990707397},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6610085964202881},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5801545977592468},{"id":"https://openalex.org/C142189719","wikidata":"https://www.wikidata.org/wiki/Q1276633","display_name":"Interpersonal ties","level":2,"score":0.486316055059433},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.45996978878974915},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40866848826408386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3930571377277374},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3783068060874939},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15888166427612305},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07541459798812866},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.061332106590270996},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.05954867601394653},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/make2020008","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make2020008","pdf_url":"https://www.mdpi.com/2504-4990/2/2/8/pdf?version=1591670153","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:99ee9bf3a7014577b5e879d8b2c0488f","is_oa":true,"landing_page_url":"https://doaj.org/article/99ee9bf3a7014577b5e879d8b2c0488f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 2, Iss 2, Pp 125-146 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2504-4990/2/2/8/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/make2020008","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/make2020008","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make2020008","pdf_url":"https://www.mdpi.com/2504-4990/2/2/8/pdf?version=1591670153","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W64143678","https://openalex.org/W91442942","https://openalex.org/W1967516159","https://openalex.org/W1988744596","https://openalex.org/W1991252559","https://openalex.org/W2026417691","https://openalex.org/W2039864426","https://openalex.org/W2059503205","https://openalex.org/W2101196063","https://openalex.org/W2101491865","https://openalex.org/W2109469951","https://openalex.org/W2112796928","https://openalex.org/W2116238870","https://openalex.org/W2137029365","https://openalex.org/W2151486346","https://openalex.org/W2154851992","https://openalex.org/W2157941589","https://openalex.org/W2159336657","https://openalex.org/W2163776610","https://openalex.org/W2253675773","https://openalex.org/W2331196207","https://openalex.org/W2468907370","https://openalex.org/W2498102315","https://openalex.org/W2533371907","https://openalex.org/W2594083602","https://openalex.org/W2618851150","https://openalex.org/W2724328101","https://openalex.org/W2799476711","https://openalex.org/W2893593062","https://openalex.org/W2898551566","https://openalex.org/W2916106175","https://openalex.org/W2919115771","https://openalex.org/W2949130492","https://openalex.org/W2962890993","https://openalex.org/W3000716014","https://openalex.org/W3011667710","https://openalex.org/W3022632415","https://openalex.org/W3104097132","https://openalex.org/W4213260080","https://openalex.org/W6603689333","https://openalex.org/W6642132090","https://openalex.org/W6724268678"],"related_works":["https://openalex.org/W4229078645","https://openalex.org/W1977345676","https://openalex.org/W4282032776","https://openalex.org/W2047552823","https://openalex.org/W4321606905","https://openalex.org/W3130445735","https://openalex.org/W2105110616","https://openalex.org/W4297905891","https://openalex.org/W2169890516","https://openalex.org/W4323542959"],"abstract_inverted_index":{"This":[0,40],"paper":[1],"explores":[2],"the":[3,12,23,45,49,71,106,122],"value":[4],"of":[5,14,25,48,66,108,119,139],"weak-ties":[6,27,128],"in":[7],"classifying":[8],"academic":[9,58],"literature":[10],"with":[11,124],"use":[13],"graph":[15],"convolutional":[16],"neural":[17],"networks.":[18],"Our":[19],"experiments":[20,96],"look":[21],"at":[22],"results":[24],"treating":[26],"as":[28],"if":[29,35],"they":[30],"were":[31],"strong-ties":[32,126],"to":[33,56,70,91],"determine":[34],"that":[36,133],"assumption":[37],"improves":[38],"performance.":[39],"is":[41,68,112],"done":[42],"by":[43,87],"applying":[44],"methodological":[46],"framework":[47],"Simplified":[50],"Graph":[51,73],"Convolutional":[52,74],"Neural":[53],"Network":[54,75],"(SGC)":[55],"two":[57],"publication":[59],"datasets:":[60],"Cora":[61],"and":[62,127],"Citeseer.":[63],"The":[64],"performance":[65,107],"SGC":[67,134],"compared":[69],"original":[72],"(GCN)":[76],"framework.":[77],"We":[78],"also":[79],"examine":[80],"how":[81],"node":[82],"removal":[83,111],"affects":[84],"prediction":[85],"accuracy":[86],"selecting":[88],"nodes":[89,101],"according":[90],"different":[92],"centrality":[93],"measures.":[94],"These":[95],"provide":[97],"insight":[98],"for":[99,105],"which":[100],"are":[102],"most":[103],"important":[104],"SGC.":[109],"When":[110],"based":[113],"on":[114],"a":[115,130],"more":[116],"localized":[117],"selection":[118],"nodes,":[120],"augmenting":[121],"network":[123,140],"both":[125],"provides":[129],"benefit,":[131],"indicating":[132],"successfully":[135],"leverages":[136],"local":[137],"information":[138],"nodes.":[141]},"counts_by_year":[{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}