{"id":"https://openalex.org/W2946529263","doi":null,"title":"Can NetGAN be improved by short random walks originated from dense vertices","display_name":"Can NetGAN be improved by short random walks originated from dense vertices","publication_year":2019,"publication_date":"2019-05-13","ids":{"openalex":"https://openalex.org/W2946529263","mag":"2946529263"},"language":"en","primary_location":{"id":"mag:2946529263","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1905.05298","pdf_url":null,"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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1905.05298","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019868919","display_name":"Amir Jalilifard","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amir Jalilifard","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023746351","display_name":"Vinicius Fernandes Carid\u00e1","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vin\u00edcius F. Carid\u00e1","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088286075","display_name":"Alex Fernandes Mansano","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alex Mansano","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5060219026","display_name":"Rogers Cristo","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rogers Cristo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.8353999853134155,"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":0.8353999853134155,"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":0.11169999837875366,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.04430000111460686,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/graph-generation","display_name":"graph generation","score":0.8040000200271606},{"id":"https://openalex.org/keywords/random-walks","display_name":"random walks","score":0.7649999856948853},{"id":"https://openalex.org/keywords/link-prediction","display_name":"link prediction","score":0.7059999704360962},{"id":"https://openalex.org/keywords/graph-learning","display_name":"graph learning","score":0.5120000243186951},{"id":"https://openalex.org/keywords/neighborhood-effects","display_name":"neighborhood effects","score":0.492000013589859}],"concepts":[{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.8397389650344849},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.5060910582542419},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4998810291290283},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4938081204891205},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4845947027206421},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4845511019229889},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.4570349454879761},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.42777368426322937},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4138299226760864},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.4129164218902588},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1730373501777649},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.15676814317703247},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15356937050819397},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06222465634346008},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2946529263","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1905.05298","pdf_url":null,"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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:2946529263","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1905.05298","pdf_url":null,"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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"score":0.699999988079071,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2997446797","https://openalex.org/W2792402990","https://openalex.org/W3200261729","https://openalex.org/W4308365798","https://openalex.org/W2786552636","https://openalex.org/W4385605993","https://openalex.org/W4400342007","https://openalex.org/W7084503503","https://openalex.org/W4414531260","https://openalex.org/W3197229351"],"abstract_inverted_index":{"Graphs":[0],"are":[1],"useful":[2],"structures":[3],"that":[4],"can":[5,74],"model":[6],"several":[7],"important":[8],"real-world":[9],"problems.":[10],"Recently,":[11],"learning":[12,26],"graphs":[13,41,60],"have":[14],"drawn":[15],"considerable":[16],"attention,":[17],"leading":[18],"to":[19,76,93,99,146],"the":[20,56,64,84,117,124,130],"proposal":[21],"of":[22,31,53,58,66,86,111,119,126,133,139],"new":[23,37,102],"methods":[24],"for":[25,39,104],"these":[27,32],"data":[28],"structures.":[29],"One":[30],"studies":[33],"produced":[34],"NetGAN,":[35],"a":[36,101,109,120],"approach":[38],"generating":[40,59],"via":[42],"random":[43,72,94,106,137],"walks.":[44],"Although":[45],"NetGAN":[46],"has":[47],"shown":[48],"promising":[49],"results":[50],"in":[51,55],"terms":[52],"accuracy":[54],"tasks":[57],"and":[61,78,153],"link":[62],"prediction,":[63],"choice":[65],"vertices":[67,132],"from":[68,108],"which":[69],"it":[70],"starts":[71],"walks":[73,87,107,138],"lead":[75],"inconsistent":[77],"highly":[79],"variable":[80],"results,":[81],"especially":[82],"when":[83],"length":[85],"is":[88],"short.":[89],"As":[90],"an":[91],"alternative":[92],"starting,":[95],"this":[96],"study":[97],"aims":[98],"establish":[100],"method":[103,144],"initializing":[105],"set":[110],"dense":[112],"vertices.":[113],"We":[114],"purpose":[115],"estimating":[116],"importance":[118],"node":[121],"based":[122],"on":[123],"inverse":[125],"its":[127,134],"influence":[128],"over":[129],"whole":[131],"neighborhood":[135],"through":[136],"different":[140],"sizes.":[141],"The":[142],"proposed":[143],"manages":[145],"achieve":[147],"significantly":[148],"better":[149],"accuracy,":[150],"less":[151],"variance":[152],"lesser":[154],"outliers.":[155]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2019-05-29T00:00:00"}