{"id":"https://openalex.org/W4414531260","doi":"https://doi.org/10.1186/s40537-025-01270-y","title":"WalkGCN: a biased sampling strategy for GNNs on non-attributed graphs","display_name":"WalkGCN: a biased sampling strategy for GNNs on non-attributed graphs","publication_year":2025,"publication_date":"2025-09-26","ids":{"openalex":"https://openalex.org/W4414531260","doi":"https://doi.org/10.1186/s40537-025-01270-y"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-025-01270-y","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01270-y","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01270-y","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01270-y","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103104631","display_name":"Mincheol Shin","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Mincheol Shin","raw_affiliation_strings":["Department of Computer Science and Engineering, Chung-Ang University, 84, Heukseok- ro, Dongjak-gu, Seoul, 06974, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Chung-Ang University, 84, Heukseok- ro, Dongjak-gu, Seoul, 06974, Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084830938","display_name":"Tae-Young Choe","orcid":"https://orcid.org/0000-0002-5253-3488"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Taeyoung Choe","raw_affiliation_strings":["Department of Computer Science and Engineering, Chung-Ang University, 84, Heukseok- ro, Dongjak-gu, Seoul, 06974, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Chung-Ang University, 84, Heukseok- ro, Dongjak-gu, Seoul, 06974, Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047926743","display_name":"Yejong Ryu","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yejong Ryu","raw_affiliation_strings":["Department of Computer Science and Engineering, Chung-Ang University, 84, Heukseok- ro, Dongjak-gu, Seoul, 06974, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Chung-Ang University, 84, Heukseok- ro, Dongjak-gu, Seoul, 06974, Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060556922","display_name":"Yanggon Kim","orcid":"https://orcid.org/0000-0003-1860-6601"},"institutions":[{"id":"https://openalex.org/I4322298","display_name":"Towson University","ror":"https://ror.org/044w7a341","country_code":"US","type":"education","lineage":["https://openalex.org/I4322298"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanggon Kim","raw_affiliation_strings":["Department of Computer and Information Sciences, Towson University, 8000 York Rd, Towson, MD, 21252, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Towson University, 8000 York Rd, Towson, MD, 21252, USA","institution_ids":["https://openalex.org/I4322298"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000615413","display_name":"Mucheol Kim","orcid":"https://orcid.org/0000-0003-2682-2195"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Mucheol Kim","raw_affiliation_strings":["Department of Computer Science and Engineering, Chung-Ang University, 84, Heukseok- ro, Dongjak-gu, Seoul, 06974, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Chung-Ang University, 84, Heukseok- ro, Dongjak-gu, Seoul, 06974, Korea","institution_ids":["https://openalex.org/I67900169"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5103104631"],"corresponding_institution_ids":["https://openalex.org/I67900169"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":2.2156,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.90706609,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"12","issue":"1","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":0.9997000098228455,"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":0.9997000098228455,"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":0.9925000071525574,"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/T12488","display_name":"Mental Health via Writing","score":0.9801999926567078,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6044999957084656},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.46650001406669617},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.43650001287460327},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4077000021934509},{"id":"https://openalex.org/keywords/random-geometric-graph","display_name":"Random geometric graph","score":0.40389999747276306},{"id":"https://openalex.org/keywords/null-model","display_name":"Null model","score":0.3959999978542328},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.3806999921798706},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.3643999993801117}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7785999774932861},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6044999957084656},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.49959999322891235},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.46650001406669617},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.43650001287460327},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4253000020980835},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4077000021934509},{"id":"https://openalex.org/C30609935","wikidata":"https://www.wikidata.org/wiki/Q7291969","display_name":"Random geometric graph","level":5,"score":0.40389999747276306},{"id":"https://openalex.org/C36382193","wikidata":"https://www.wikidata.org/wiki/Q7068966","display_name":"Null model","level":2,"score":0.3959999978542328},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.3806999921798706},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3643999993801117},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.3407000005245209},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.328000009059906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3240000009536743},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.2948000133037567},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28529998660087585},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C137753397","wikidata":"https://www.wikidata.org/wiki/Q2434424","display_name":"Network science","level":3,"score":0.27070000767707825},{"id":"https://openalex.org/C2779812341","wikidata":"https://www.wikidata.org/wiki/Q5891525","display_name":"Homophily","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.2605000138282776}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-025-01270-y","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01270-y","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01270-y","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:7041f2bf5d6243ad9bd59e82ef7490ec","is_oa":true,"landing_page_url":"https://doaj.org/article/7041f2bf5d6243ad9bd59e82ef7490ec","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 12, Iss 1, Pp 1-15 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-025-01270-y","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01270-y","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01270-y","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321202","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414531260.pdf","grobid_xml":"https://content.openalex.org/works/W4414531260.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W2090891622","https://openalex.org/W2146332392","https://openalex.org/W2154851992","https://openalex.org/W2162630660","https://openalex.org/W2168190036","https://openalex.org/W2387462954","https://openalex.org/W2700550412","https://openalex.org/W2788614083","https://openalex.org/W2792234394","https://openalex.org/W2907492528","https://openalex.org/W2962756421","https://openalex.org/W2998336824","https://openalex.org/W3040731923","https://openalex.org/W3092835783","https://openalex.org/W3098702884","https://openalex.org/W3102794461","https://openalex.org/W3104097132","https://openalex.org/W3128866225","https://openalex.org/W3155886566","https://openalex.org/W3172133997","https://openalex.org/W3210845443","https://openalex.org/W4214514473","https://openalex.org/W4282000482","https://openalex.org/W4306317025","https://openalex.org/W4323669763","https://openalex.org/W4382239927","https://openalex.org/W4386914062","https://openalex.org/W4391528789","https://openalex.org/W4392384599","https://openalex.org/W4396670147","https://openalex.org/W4408792687"],"related_works":[],"abstract_inverted_index":{"Graph":[0,61,101],"Neural":[1],"Networks":[2,63,103],"(GNNs)":[3],"typically":[4],"assume":[5],"the":[6,66,83,89,126,134,142,162,192,197,201],"presence":[7],"of":[8,44,69,85,91,129,174,191,196,207],"node":[9,108,121,138,170],"attributes":[10,35,59,149,157,187],"to":[11,31,56,132,161],"capture":[12],"interactions":[13],"in":[14,60,141],"a":[15,42,78,112,205],"graph":[16,20,154,164,179],"structure.":[17],"However,":[18,65],"real-world":[19],"data":[21],"often":[22],"has":[23],"incomplete":[24,34],"or":[25,194],"completely-missing":[26],"attribute":[27],"information.":[28],"GNN":[29],"approaches":[30],"dealing":[32],"with":[33,155],"are":[36,145,189],"widely":[37],"implemented,":[38],"yet":[39],"there":[40],"is":[41,158,212],"paucity":[43],"research":[45],"on":[46,177],"graphs":[47],"without":[48],"attributes.":[49,109],"DeepWalk":[50],"and":[51,88],"node2vec":[52],"can":[53],"be":[54],"exploited":[55],"generate":[57],"artificial":[58,107,148,156,186],"Convolutional":[62,102],"(GCN).":[64],"stochastic":[67],"nature":[68],"random":[70,198],"walks":[71],"disrupts":[72],"consistent":[73],"performance.":[74],"Furthermore,":[75,200],"it":[76],"introduces":[77],"degree":[79],"bias,":[80],"which":[81,168],"causes":[82],"over-sampling":[84],"hub":[86],"nodes":[87,131],"under-representation":[90],"low-degree":[92],"nodes.":[93],"To":[94],"address":[95],"this":[96],"limitation,":[97],"we":[98],"propose":[99],"Walk":[100],"(WalkGCN)":[104],"for":[105,150],"generating":[106],"WalkGCN":[110,183],"employs":[111],"biased":[113],"sampling":[114,127],"strategy":[115],"that":[116,182,188],"mitigates":[117],"degree-induced":[118],"bias":[119],"during":[120],"sequence":[122],"generation.":[123],"It":[124],"increases":[125],"frequency":[128],"leaf":[130],"balance":[133],"training":[135],"data.":[136],"The":[137,153,172,210],"embeddings":[139],"generated":[140],"preceding":[143],"phase":[144],"employed":[146],"as":[147],"each":[151],"node.":[152],"then":[159],"fed":[160],"vanilla":[163],"convolutional":[165],"networks":[166],"model,":[167],"performs":[169],"classification.":[171],"results":[173],"extensive":[175],"experiments":[176],"three":[178],"datasets":[180],"demonstrate":[181],"effectively":[184],"generates":[185],"independent":[190],"hyperparameters":[193],"randomness":[195],"walk.":[199],"proposed":[202],"model":[203],"outperforms":[204],"variety":[206],"baseline":[208],"models.":[209],"code":[211],"available":[213],"at":[214],"https://github.com/mincheol-shin-cau/WalkGCN":[215],".":[216]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-22T08:38:42.863108","created_date":"2025-10-10T00:00:00"}