{"id":"https://openalex.org/W4401863334","doi":"https://doi.org/10.1145/3637528.3671682","title":"Self-Supervised Learning for Graph Dataset Condensation","display_name":"Self-Supervised Learning for Graph Dataset Condensation","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401863334","doi":"https://doi.org/10.1145/3637528.3671682"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671682","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081081290","display_name":"Yuxiang Wang","orcid":"https://orcid.org/0000-0002-5483-8322"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuxiang Wang","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100367774","display_name":"Xiao Yan","orcid":"https://orcid.org/0000-0002-2122-915X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao Yan","raw_affiliation_strings":["Centre for Perceptual and Interactive Intelligence (CPII), Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Centre for Perceptual and Interactive Intelligence (CPII), Hong Kong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028127634","display_name":"Shiyu Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiyu Jin","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082976295","display_name":"Hao Huang","orcid":"https://orcid.org/0000-0002-3777-1488"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Huang","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074979952","display_name":"Quanqing Xu","orcid":"https://orcid.org/0000-0001-8989-9662"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quanqing Xu","raw_affiliation_strings":["OceanBase, Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"OceanBase, Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101829682","display_name":"Qingchen Zhang","orcid":"https://orcid.org/0000-0001-5525-687X"},"institutions":[{"id":"https://openalex.org/I20942203","display_name":"Hainan University","ror":"https://ror.org/03q648j11","country_code":"CN","type":"education","lineage":["https://openalex.org/I20942203"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingchen Zhang","raw_affiliation_strings":["School of Computer Science and Technology, Hainan University, Haikou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Hainan University, Haikou, China","institution_ids":["https://openalex.org/I20942203"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007642002","display_name":"Bo Du","orcid":"https://orcid.org/0000-0001-8104-3448"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Du","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102918834","display_name":"Jiawei Jiang","orcid":"https://orcid.org/0000-0003-0051-0046"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Jiang","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5081081290"],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":2.7801,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.91594468,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3289","last_page":"3298"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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":0.9998999834060669,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9937999844551086,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9915000200271606,"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/computer-science","display_name":"Computer science","score":0.653717577457428},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.532333254814148},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4412006139755249},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4156021475791931},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.28136610984802246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.653717577457428},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.532333254814148},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4412006139755249},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4156021475791931},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28136610984802246}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3671682","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W3464401","https://openalex.org/W1997865285","https://openalex.org/W2008857988","https://openalex.org/W2097308346","https://openalex.org/W2405933695","https://openalex.org/W2807021761","https://openalex.org/W2911286998","https://openalex.org/W2997591727","https://openalex.org/W3021975806","https://openalex.org/W3095602948","https://openalex.org/W3100078588","https://openalex.org/W3100848837","https://openalex.org/W3130274530","https://openalex.org/W3165369424","https://openalex.org/W4226218349","https://openalex.org/W4283076909","https://openalex.org/W4297733535","https://openalex.org/W4309609199","https://openalex.org/W4319300193","https://openalex.org/W4385567993","https://openalex.org/W4385973160","https://openalex.org/W6784694379"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Graph":[0],"dataset":[1,6,13,68,90],"condensation":[2,69,91],"(GDC)":[3],"reduces":[4,170],"a":[5,11,65,99,140,159,191],"with":[7,14,83,134,175,232],"many":[8,56],"graphs":[9,16,133,203,207],"into":[10],"smaller":[12],"fewer":[15],"while":[17,49],"maintaining":[18],"model":[19,225],"training":[20,124],"accuracy.":[21],"GDC":[22,33],"saves":[23],"the":[24,47,84,106,127,131,135,147,198,201,205,249],"storage":[25],"cost":[26],"and":[27,42,92,152,184],"hence":[28],"accelerates":[29],"training.":[30,214],"Although":[31],"several":[32],"methods":[34],"have":[35],"been":[36],"proposed,":[37],"they":[38,238],"are":[39],"all":[40],"supervised":[41],"require":[43,76],"massive":[44],"labels":[45,51],"for":[46,89,149,204],"graphs,":[48],"graph":[50,67,160,219],"can":[52],"be":[53],"scarce":[54],"in":[55],"practical":[57],"scenarios.":[58],"To":[59,114,181],"fill":[60],"this":[61,116],"gap,":[62],"we":[63,118,189],"propose":[64,158],"self-supervised":[66,176],"method":[70],"called":[71],"SGDC,":[72],"which":[73,122,163,196],"does":[74],"not":[75,164],"label":[77,240],"information.":[78,241],"Our":[79,215],"initial":[80],"design":[81,145],"starts":[82],"classical":[85],"bilevel":[86],"optimization":[87],"paradigm":[88],"incorporates":[93],"contrastive":[94],"learning":[95,211],"techniques.":[96],"But":[97],"such":[98],"solution":[100],"yields":[101],"poor":[102],"accuracy":[103,167,226],"due":[104],"to":[105,229],"biased":[107,154],"gradient":[108],"estimation":[109],"caused":[110],"by":[111,125,130,139,227],"data":[112,150],"augmentation.":[113],"solve":[115],"problem,":[117],"introduce":[119],"representation":[120],"matching,":[121],"conducts":[123],"aligning":[126],"representations":[128,137],"produced":[129],"condensed":[132,206],"target":[136],"generated":[138],"pre-trained":[141],"SSL":[142],"model.":[143],"This":[144],"eliminates":[146],"need":[148],"augmentation":[151],"avoids":[153],"gradient.":[155],"We":[156],"further":[157],"attention":[161],"kernel,":[162],"only":[165],"improves":[166,224],"but":[168],"also":[169],"running":[171],"time":[172],"when":[173],"combined":[174],"kernel":[177],"ridge":[178],"regression":[179],"(KRR).":[180],"simplify":[182],"SGDC":[183,223,243],"make":[185],"it":[186],"more":[187,246],"robust,":[188],"adopt":[190],"adjacency":[192],"matrix":[193],"reusing":[194],"approach,":[195],"reuses":[197],"topology":[199,212],"of":[200,209],"original":[202],"instead":[208],"repeatedly":[210],"during":[213],"evaluations":[216],"on":[217],"seven":[218],"datasets":[220],"find":[221],"that":[222],"up":[228],"9.7%":[230],"compared":[231],"5":[233],"state-of-the-art":[234],"baselines,":[235],"even":[236],"if":[237],"use":[239],"Moreover,":[242],"is":[244],"significantly":[245],"efficient":[247],"than":[248],"baselines.":[250]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
