{"id":"https://openalex.org/W4403570418","doi":"https://doi.org/10.48550/arxiv.2410.10089","title":"PromptGCN: Bridging Subgraph Gaps in Lightweight GCNs","display_name":"PromptGCN: Bridging Subgraph Gaps in Lightweight GCNs","publication_year":2024,"publication_date":"2024-10-13","ids":{"openalex":"https://openalex.org/W4403570418","doi":"https://doi.org/10.48550/arxiv.2410.10089"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2410.10089","pdf_url":"http://arxiv.org/pdf/2410.10089","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/pdf/2410.10089","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103205936","display_name":"Shixin Ji","orcid":"https://orcid.org/0009-0003-3429-4692"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji, Shengwei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102001776","display_name":"Yujie Tian","orcid":"https://orcid.org/0000-0002-4466-8833"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Yujie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115602474","display_name":"Fei Liu","orcid":"https://orcid.org/0000-0003-1175-4070"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Fei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103136479","display_name":"Xinlu Li","orcid":"https://orcid.org/0000-0003-1704-6112"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xinlu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5021388128","display_name":"Le Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Le","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"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":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":77},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.976,"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":0.976,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9571,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.89257276},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.37130648},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.32534885},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.23166159},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.16311905}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2410.10089","pdf_url":"http://arxiv.org/pdf/2410.10089","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2410.10089","pdf_url":"http://arxiv.org/pdf/2410.10089","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W4388870064","https://openalex.org/W4235186151","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2667588871","https://openalex.org/W2272354214","https://openalex.org/W2210139803","https://openalex.org/W2056057048","https://openalex.org/W2054685365"],"abstract_inverted_index":{"Graph":[0],"Convolutional":[1],"Networks":[2],"(GCNs)":[3],"are":[4,126,135],"widely":[5],"used":[6],"in":[7,24],"graph-based":[8],"applications,":[9],"such":[10],"as":[11],"social":[12],"networks":[13],"and":[14,69],"recommendation":[15],"systems.":[16],"Nevertheless,":[17],"large-scale":[18],"graphs":[19,153],"or":[20],"deep":[21],"aggregation":[22],"layers":[23],"full-batch":[25],"GCNs":[26,61,72,84],"consume":[27],"significant":[28],"GPU":[29],"memory,":[30],"causing":[31],"out":[32],"of":[33,93,101,168],"memory":[34,42,56],"(OOM)":[35],"errors":[36],"on":[37,44,73,90,150,176],"mainstream":[38],"GPUs":[39],"(e.g.,":[40],"29GB":[41],"consumption":[43,57],"the":[45,64,99,117,122,133,142,166,177],"Ogbnproducts":[46],"graph":[47,65,95],"with":[48,186,197],"5":[49],"layers).":[50],"The":[51],"subgraph":[52,139,169,188],"sampling":[53,170,189],"methods":[54,78,171],"reduce":[55],"to":[58,115,128,140,161,174,191],"achieve":[59],"lightweight":[60,112,194],"by":[62,172],"partitioning":[63],"into":[66,137],"multiple":[67],"subgraphs":[68,91],"sequentially":[70],"training":[71],"each":[74,138],"subgraph.":[75],"However,":[76],"these":[77],"yield":[79],"gaps":[80,118],"among":[81,119,145],"subgraphs,":[82],"i.e.,":[83],"can":[85,182],"only":[86],"be":[87,183],"trained":[88],"based":[89],"instead":[92],"global":[94,130,143],"information,":[96],"which":[97],"reduces":[98],"accuracy":[100,167],"GCNs.":[102],"In":[103],"this":[104],"paper,":[105],"we":[106],"propose":[107],"PromptGCN,":[108],"a":[109,193],"novel":[110],"prompt-based":[111],"GCN":[113,195],"model":[114,196],"bridge":[116],"subgraphs.":[120,146],"First,":[121],"learnable":[123],"prompt":[124],"embeddings":[125],"designed":[127],"obtain":[129,192],"information.":[131],"Then,":[132],"prompts":[134],"attached":[136],"transfer":[141],"information":[144],"Extensive":[147],"experimental":[148],"results":[149],"seven":[151],"largescale":[152],"demonstrate":[154],"that":[155],"PromptGCN":[156,164,181],"exhibits":[157],"superior":[158],"performance":[159],"compared":[160],"baselines.":[162],"Notably,":[163],"improves":[165],"up":[173],"5.48%":[175],"Flickr":[178],"dataset.":[179],"Overall,":[180],"easily":[184],"combined":[185],"any":[187],"method":[190],"higher":[198],"accuracy.":[199]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4403570418","counts_by_year":[],"updated_date":"2025-04-22T19:14:59.866792","created_date":"2024-10-20"}