{"id":"https://openalex.org/W4386546292","doi":"https://doi.org/10.21203/rs.3.rs-3288680/v1","title":"Attributed Labeled BTER-Based Generative Model for Benchmarking of Graph Neural Networks","display_name":"Attributed Labeled BTER-Based Generative Model for Benchmarking of Graph Neural Networks","publication_year":2023,"publication_date":"2023-09-08","ids":{"openalex":"https://openalex.org/W4386546292","doi":"https://doi.org/10.21203/rs.3.rs-3288680/v1"},"language":"en","primary_location":{"id":"doi:10.21203/rs.3.rs-3288680/v1","doi":"https://doi.org/10.21203/rs.3.rs-3288680/v1","is_oa":true,"landing_page_url":"http://dx.doi.org/10.21203/rs.3.rs-3288680/v1","pdf_url":"https://www.researchsquare.com/article/rs-3288680/latest.pdf","source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Research Square","raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.researchsquare.com/article/rs-3288680/latest.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102553756","display_name":"Polina Andreeva","orcid":"https://orcid.org/0000-0002-3130-1012","observed_orcids":["https://orcid.org/0000-0002-3130-1012"]},"institutions":[{"id":"https://openalex.org/I173089394","display_name":"ITMO University","ror":"https://ror.org/04txgxn49","country_code":"RU","type":"education","lineage":["https://openalex.org/I173089394"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Polina Andreeva","raw_affiliation_strings":["ITMO University","National Center for Cognitive Research, ITMO University, Street, Saint-Petersburg, 199034, Russian Federation"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ITMO University","institution_ids":["https://openalex.org/I173089394"]},{"raw_affiliation_string":"National Center for Cognitive Research, ITMO University, Street, Saint-Petersburg, 199034, Russian Federation","institution_ids":["https://openalex.org/I173089394"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055652750","display_name":"Klavdiya Bochenina","orcid":"https://orcid.org/0000-0001-6025-0552","observed_orcids":["https://orcid.org/0000-0001-6025-0552"]},"institutions":[{"id":"https://openalex.org/I173089394","display_name":"ITMO University","ror":"https://ror.org/04txgxn49","country_code":"RU","type":"education","lineage":["https://openalex.org/I173089394"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Klavdiya Bochenina","raw_affiliation_strings":["ITMO University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ITMO University","institution_ids":["https://openalex.org/I173089394"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039016248","display_name":"Egor Shikov","orcid":"https://orcid.org/0000-0001-5749-4222","observed_orcids":["https://orcid.org/0000-0001-5749-4222"]},"institutions":[{"id":"https://openalex.org/I173089394","display_name":"ITMO University","ror":"https://ror.org/04txgxn49","country_code":"RU","type":"education","lineage":["https://openalex.org/I173089394"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Egor Shikov","raw_affiliation_strings":["ITMO University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ITMO University","institution_ids":["https://openalex.org/I173089394"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5102553756"],"corresponding_institution_ids":["https://openalex.org/I173089394"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"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":0.6894999742507935,"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":0.6894999742507935,"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":0.27250000834465027,"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":0.03359999880194664,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.8080000281333923},{"id":"https://openalex.org/keywords/benchmark-dataset","display_name":"benchmark dataset","score":0.7509999871253967},{"id":"https://openalex.org/keywords/generative-models","display_name":"generative models","score":0.7200000286102295},{"id":"https://openalex.org/keywords/attributed-networks","display_name":"attributed graphs","score":0.9580000042915344},{"id":"https://openalex.org/keywords/graph-structural-properties","display_name":"graph structural properties","score":0.6729999780654907},{"id":"https://openalex.org/keywords/clustering-coefficient","display_name":"clustering coefficient","score":0.9309999942779541},{"id":"https://openalex.org/keywords/shortest-path-length","display_name":"shortest path length","score":0.7680000066757202},{"id":"https://openalex.org/keywords/average-degree","display_name":"average degree","score":0.6949999928474426},{"id":"https://openalex.org/keywords/synthetic-graph-generation","display_name":"synthetic graph generation","score":0.5600000023841858}],"concepts":[{"id":"https://openalex.org/C89694873","wikidata":"https://www.wikidata.org/wiki/Q4810299","display_name":"Assortativity","level":3,"score":0.9474186897277832},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6013644933700562},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5685269236564636},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5641564130783081},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5484944581985474},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5210620760917664},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.5166852474212646},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46644067764282227},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4338158369064331},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4173271358013153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3638232350349426},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34916985034942627},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32934096455574036},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.23222747445106506},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","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/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21203/rs.3.rs-3288680/v1","doi":"https://doi.org/10.21203/rs.3.rs-3288680/v1","is_oa":true,"landing_page_url":"http://dx.doi.org/10.21203/rs.3.rs-3288680/v1","pdf_url":"https://www.researchsquare.com/article/rs-3288680/latest.pdf","source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Research Square","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.21203/rs.3.rs-3288680/v1","doi":"https://doi.org/10.21203/rs.3.rs-3288680/v1","is_oa":true,"landing_page_url":"http://dx.doi.org/10.21203/rs.3.rs-3288680/v1","pdf_url":"https://www.researchsquare.com/article/rs-3288680/latest.pdf","source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Research Square","raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G222280408","display_name":null,"funder_award_id":"#17-71-30029","funder_id":"https://openalex.org/F4320324099","funder_display_name":"Russian Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320324099","display_name":"Russian Science Foundation","ror":"https://ror.org/03y2gwe85"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386546292.pdf","grobid_xml":"https://content.openalex.org/works/W4386546292.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1701054150","https://openalex.org/W1976176606","https://openalex.org/W1985339741","https://openalex.org/W2022704179","https://openalex.org/W2023655578","https://openalex.org/W2025266141","https://openalex.org/W2027377866","https://openalex.org/W2027731328","https://openalex.org/W2042857706","https://openalex.org/W2047818868","https://openalex.org/W2108614537","https://openalex.org/W2112090702","https://openalex.org/W2112976607","https://openalex.org/W2119998616","https://openalex.org/W2130354913","https://openalex.org/W2130687290","https://openalex.org/W2168190036","https://openalex.org/W2519887557","https://openalex.org/W2537623931","https://openalex.org/W2766453196","https://openalex.org/W2784007581","https://openalex.org/W2883384506","https://openalex.org/W2912083425","https://openalex.org/W2949676527","https://openalex.org/W2998755720","https://openalex.org/W3090016979","https://openalex.org/W3114928288","https://openalex.org/W3128443161","https://openalex.org/W3142570770","https://openalex.org/W3144257051","https://openalex.org/W3152893301","https://openalex.org/W4289389616","https://openalex.org/W6602311867","https://openalex.org/W6736759262","https://openalex.org/W6839257546"],"related_works":["https://openalex.org/W7212321160","https://openalex.org/W4360982845","https://openalex.org/W2025266141","https://openalex.org/W4414531260","https://openalex.org/W4384807749","https://openalex.org/W2979054879","https://openalex.org/W2601983445","https://openalex.org/W4387928511","https://openalex.org/W7099737162","https://openalex.org/W3200513127"],"abstract_inverted_index":{"Abstract":[0],"Graph":[1],"Neural":[2],"Networks":[3],"(GNNs)":[4],"have":[5],"become":[6],"increasingly":[7],"popular":[8],"for":[9,51,79],"tasks":[10],"such":[11],"as":[12],"link":[13],"prediction,":[14],"node":[15],"classification,":[16],"and":[17,110,134],"graph":[18,39,61,97,128],"generation.":[19],"However,":[20],"a":[21,56,76,114],"number":[22,95],"of":[23,59,96,143],"models":[24],"show":[25],"weak":[26],"performance":[27],"on":[28,83],"graphs":[29,81],"with":[30,131],"low":[31],"assortativity":[32,139],"measure.":[33],"At":[34],"the":[35],"same":[36],"time,":[37],"other":[38,126],"characteristics":[40,99],"may":[41],"also":[42],"influence":[43],"GNN":[44],"quality.":[45],"Therefore,":[46],"it":[47],"is":[48],"extremely":[49],"important":[50],"benchmark":[52],"datasets":[53],"to":[54,92,124,136,141],"cover":[55],"wide":[57],"range":[58],"different":[60],"properties,":[62],"which":[63],"can":[64,121],"not":[65],"be":[66,122],"provided":[67],"by":[68],"real-world":[69],"sources.":[70],"In":[71],"this":[72],"paper,":[73],"we":[74],"present":[75],"generative":[77,119,129],"model":[78,89,130],"attributed":[80],"based":[82],"Block":[84],"Two-Level":[85],"Erd\\H{o}s-R\\'enyi":[86],"model.":[87],"Our":[88,117],"allows":[90,135],"one":[91],"vary":[93],"larger":[94],"structural":[98],"(namely,":[100],"clustering":[101],"coefficient,":[102],"average":[103,105],"degree,":[104],"shortest":[106],"paths":[107],"length,":[108],"label":[109],"attribute":[111,118,138],"assortativity)":[112],"in":[113],"wider":[115],"range.":[116],"method":[120],"applied":[123],"any":[125],"non-attributed":[127],"community":[132],"structure":[133,142],"control":[137],"corresponding":[140],"graph.":[144]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2023-09-09T00:00:00"}