{"id":"https://openalex.org/W6948323877","doi":"https://doi.org/10.48550/arxiv.2503.06614","title":"Using Subgraph GNNs for Node Classification:an Overlooked Potential Approach","display_name":"Using Subgraph GNNs for Node Classification:an Overlooked Potential Approach","publication_year":2025,"publication_date":"2025-03-09","ids":{"openalex":"https://openalex.org/W6948323877","doi":"https://doi.org/10.48550/arxiv.2503.06614"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2503.06614","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2503.06614","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2503.06614","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zeng, Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zeng, Qian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Lin, Xin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Xin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Gao, Jingyi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Jingyi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Yu, Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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":true,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9768999814987183,"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":0.9768999814987183,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.005200000014156103,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.002300000051036477,"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/scalability","display_name":"Scalability","score":0.7447999715805054},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6241000294685364},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5838000178337097},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47130000591278076},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46619999408721924},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.45989999175071716},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4551999866962433},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.42969998717308044},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4065999984741211}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7621999979019165},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7447999715805054},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6241000294685364},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5838000178337097},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47130000591278076},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46619999408721924},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.45989999175071716},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4551999866962433},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4388999938964844},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.42969998717308044},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41589999198913574},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4065999984741211},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4056999981403351},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34369999170303345},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31349998712539673},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C16311509","wikidata":"https://www.wikidata.org/wiki/Q4148050","display_name":"Dependency graph","level":3,"score":0.288100004196167},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2759000062942505},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2503.06614","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2503.06614","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2503.06614","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2503.06614","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Previous":[0],"studies":[1],"have":[2,45],"demonstrated":[3],"the":[4,70,94],"strong":[5],"performance":[6,66,153],"of":[7,53,72],"Graph":[8],"Neural":[9],"Networks":[10],"(GNNs)":[11],"in":[12,162],"node":[13,95,175],"classification.":[14,176],"However,":[15],"most":[16],"existing":[17],"GNNs":[18],"adopt":[19],"a":[20,99,113,171],"node-centric":[21],"perspective":[22],"and":[23,33,89,103,117,168],"rely":[24],"on":[25,140,145],"global":[26,73,81,158],"message":[27],"passing,":[28],"leading":[29],"to":[30,69,80,123,134,155],"high":[31],"computational":[32,55],"memory":[34],"costs":[35],"that":[36,150],"hinder":[37],"scalability.":[38],"To":[39,83],"mitigate":[40],"these":[41],"challenges,":[42],"subgraph-based":[43],"methods":[44],"been":[46],"introduced,":[47],"leveraging":[48],"local":[49],"subgraphs":[50],"as":[51,98,170],"approximations":[52],"full":[54],"trees.":[56],"While":[57],"this":[58,85],"approach":[59],"improves":[60],"efficiency,":[61],"it":[62],"often":[63],"suffers":[64],"from":[65],"degradation":[67],"due":[68],"loss":[71],"contextual":[74],"information,":[75],"limiting":[76],"its":[77,166],"effectiveness":[78,167],"compared":[79],"GNNs.":[82],"address":[84],"trade-off":[86],"between":[87],"scalability":[88,169],"classification":[90,96,101],"accuracy,":[91],"we":[92],"reformulate":[93],"task":[97],"subgraph":[100,120],"problem":[102],"propose":[104],"SubGND":[105,151],"(Subgraph":[106],"GNN":[107],"for":[108,174],"NoDe).":[109],"This":[110],"framework":[111],"introduces":[112],"differentiated":[114],"zero-padding":[115],"strategy":[116],"an":[118,129],"Ego-Alter":[119],"representation":[121],"method":[122],"resolve":[124],"label":[125],"conflicts":[126],"while":[127],"incorporating":[128],"Adaptive":[130],"Feature":[131],"Scaling":[132],"Mechanism":[133],"dynamically":[135],"adjust":[136],"feature":[137],"contributions":[138],"based":[139],"dataset-specific":[141],"dependencies.":[142],"Experimental":[143],"results":[144],"six":[146],"benchmark":[147],"datasets":[148],"demonstrate":[149],"achieves":[152],"comparable":[154],"or":[156],"surpassing":[157],"message-passing":[159],"GNNs,":[160],"particularly":[161],"heterophilic":[163],"settings,":[164],"highlighting":[165],"promising":[172],"solution":[173]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}