{"id":"https://openalex.org/W4378942422","doi":"https://doi.org/10.1145/3580305.3599410","title":"Learning Strong Graph Neural Networks with Weak Information","display_name":"Learning Strong Graph Neural Networks with Weak Information","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4378942422","doi":"https://doi.org/10.1145/3580305.3599410"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599410","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2305.18457","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100386682","display_name":"Yixin Liu","orcid":"https://orcid.org/0000-0002-4309-5076"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Yixin Liu","raw_affiliation_strings":["Monash University, Melbourne, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Monash University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044455276","display_name":"Kaize Ding","orcid":"https://orcid.org/0000-0001-6684-6752"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kaize Ding","raw_affiliation_strings":["Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101918345","display_name":"Jianling Wang","orcid":"https://orcid.org/0000-0001-9916-0976"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianling Wang","raw_affiliation_strings":["Texas A&amp;M University, College Station, TX, USA"],"affiliations":[{"raw_affiliation_string":"Texas A&amp;M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045979794","display_name":"Vincent C. S. Lee","orcid":"https://orcid.org/0000-0001-5976-4601"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Vincent Lee","raw_affiliation_strings":["Monash University, Melbourne, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Monash University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100338946","display_name":"Huan Liu","orcid":"https://orcid.org/0000-0002-3264-7904"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huan Liu","raw_affiliation_strings":["Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008056593","display_name":"Shirui Pan","orcid":"https://orcid.org/0000-0003-0794-527X"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shirui Pan","raw_affiliation_strings":["Griffith University, Gold Coast, Australia"],"affiliations":[{"raw_affiliation_string":"Griffith University, Gold Coast, Australia","institution_ids":["https://openalex.org/I11701301"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100386682"],"corresponding_institution_ids":["https://openalex.org/I56590836"],"apc_list":null,"apc_paid":null,"fwci":6.7113,"has_fulltext":true,"cited_by_count":40,"citation_normalized_percentile":{"value":0.97504648,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1559","last_page":"1571"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"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":1.0,"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":0.986299991607666,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9854000210762024,"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":0.6750664710998535},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4947461783885956},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.48247411847114563},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3821565508842468},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3350626826286316}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6750664710998535},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4947461783885956},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.48247411847114563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3821565508842468},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3350626826286316}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3580305.3599410","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2305.18457","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.18457","pdf_url":"https://arxiv.org/pdf/2305.18457","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:research-repository.griffith.edu.au:10072/426087","is_oa":true,"landing_page_url":"http://hdl.handle.net/10072/426087","pdf_url":null,"source":{"id":"https://openalex.org/S4306402548","display_name":"Griffith Research Online (Griffith University, Queensland, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I11701301","host_organization_name":"Griffith University","host_organization_lineage":["https://openalex.org/I11701301"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference output"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2305.18457","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.18457","pdf_url":"https://arxiv.org/pdf/2305.18457","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2932374280","display_name":null,"funder_award_id":"N00014-21-1-4002","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G5335404683","display_name":null,"funder_award_id":"FT210100097","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G5429966900","display_name":null,"funder_award_id":"2229461","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6894402473","display_name":null,"funder_award_id":"Fellowship","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8624481510","display_name":"CNH:    Collaborative Research: Northern Gulf of Mexico Hypoxia and Land Use in the Watershed: Feedback and Scale Interactions","funder_award_id":"1010009","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4378942422.pdf","grobid_xml":"https://content.openalex.org/works/W4378942422.grobid-xml"},"referenced_works_count":81,"referenced_works":["https://openalex.org/W2153959628","https://openalex.org/W2187089797","https://openalex.org/W2799321842","https://openalex.org/W2900470550","https://openalex.org/W2916106175","https://openalex.org/W2924719072","https://openalex.org/W2943501111","https://openalex.org/W2945827377","https://openalex.org/W2961295589","https://openalex.org/W2962711740","https://openalex.org/W2963757395","https://openalex.org/W2964015378","https://openalex.org/W2964051675","https://openalex.org/W2979683452","https://openalex.org/W2990045899","https://openalex.org/W2998269939","https://openalex.org/W3021975806","https://openalex.org/W3034492151","https://openalex.org/W3036106327","https://openalex.org/W3039500550","https://openalex.org/W3040731923","https://openalex.org/W3044450160","https://openalex.org/W3081203761","https://openalex.org/W3092835783","https://openalex.org/W3093814892","https://openalex.org/W3094678421","https://openalex.org/W3098766148","https://openalex.org/W3098797593","https://openalex.org/W3099064659","https://openalex.org/W3100078588","https://openalex.org/W3100646853","https://openalex.org/W3101543043","https://openalex.org/W3101553402","https://openalex.org/W3114928288","https://openalex.org/W3121199044","https://openalex.org/W3124006607","https://openalex.org/W3135138557","https://openalex.org/W3153206160","https://openalex.org/W3155886566","https://openalex.org/W3160021293","https://openalex.org/W3172133997","https://openalex.org/W3196261868","https://openalex.org/W3208322257","https://openalex.org/W3212515727","https://openalex.org/W3217672792","https://openalex.org/W4210257598","https://openalex.org/W4212852078","https://openalex.org/W4221145116","https://openalex.org/W4221150249","https://openalex.org/W4221166060","https://openalex.org/W4225657277","https://openalex.org/W4250735360","https://openalex.org/W4280535549","https://openalex.org/W4283796083","https://openalex.org/W4287330167","https://openalex.org/W4287754915","https://openalex.org/W4287757758","https://openalex.org/W4288088467","https://openalex.org/W4289389616","https://openalex.org/W4290874903","https://openalex.org/W4290943435","https://openalex.org/W4293574903","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W4297899355","https://openalex.org/W4308161732","https://openalex.org/W4308671321","https://openalex.org/W4309302775","https://openalex.org/W4311079930","https://openalex.org/W4318823295","https://openalex.org/W4318828927","https://openalex.org/W4320463723","https://openalex.org/W4366327286","https://openalex.org/W4367047312","https://openalex.org/W4382239158","https://openalex.org/W4382239867","https://openalex.org/W4382239927","https://openalex.org/W4382318655","https://openalex.org/W6600009415","https://openalex.org/W6600134738","https://openalex.org/W6807384801"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"have":[4],"exhibited":[5],"impressive":[6],"performance":[7,15],"in":[8,59,78,124,231],"many":[9],"graph":[10,23,46,94,158,167],"learning":[11,95],"tasks.":[12],"Nevertheless,":[13],"the":[14,21,45,62,76,91,102,116,136,156,183,192,205,212,223],"of":[16,52,93,118,227],"GNNs":[17,125],"can":[18,198,209],"deteriorate":[19],"when":[20],"input":[22,157],"data":[24,47,66],"suffer":[25],"from":[26,44,57,104,135,187,201],"weak":[27,53,97],"information,":[28,54],"i.e.,":[29,120],"incomplete":[30,32,160],"structure,":[31,161],"features,":[33],"and":[34,69,87,126,204,225],"insufficient":[35],"labels.":[36],"Most":[37],"prior":[38],"studies,":[39],"which":[40],"attempt":[41],"to":[42,83,90,130],"learn":[43],"with":[48,61,96,159],"a":[49,144,165,176],"specific":[50],"type":[51],"are":[55],"far":[56],"effective":[58,86],"dealing":[60],"scenario":[63],"where":[64],"diverse":[65],"deficiencies":[67],"exist":[68],"mutually":[70,199],"affect":[71],"each":[72,202],"other.":[73],"To":[74],"fill":[75],"gap,":[77],"this":[79],"paper,":[80],"we":[81,108,141],"aim":[82],"develop":[84,175],"an":[85],"principled":[88],"approach":[89],"problem":[92,117],"information":[98,128,151,195],"(GLWI).":[99],"Based":[100],"on":[101,155,164,217],"findings":[103],"our":[105,228],"empirical":[106],"analysis,":[107],"derive":[109],"two":[110,188,193],"design":[111],"focal":[112],"points":[113],"for":[114],"solving":[115],"GLWI,":[119],"enabling":[121],"long-range":[122,150],"propagation":[123,129,152,196],"allowing":[127],"those":[131],"stray":[132],"nodes":[133],"isolated":[134],"largest":[137],"connected":[138],"component.":[139],"Accordingly,":[140],"propose":[142],"D2PT,":[143],"dual-channel":[145],"GNN":[146],"framework":[147],"that":[148,168,181,191],"performs":[149],"not":[153],"only":[154],"but":[162],"also":[163],"global":[166,170],"encodes":[169],"semantic":[171],"similarities.":[172],"We":[173],"further":[174],"prototype":[177],"contrastive":[178],"alignment":[179],"algorithm":[180],"aligns":[182],"class-level":[184],"prototypes":[185],"learned":[186,207],"channels,":[189],"such":[190],"different":[194],"processes":[197],"benefit":[200],"other":[203],"finally":[206],"model":[208],"well":[210],"handle":[211],"GLWI":[213,233],"problem.":[214],"Extensive":[215],"experiments":[216],"eight":[218],"real-world":[219],"benchmark":[220],"datasets":[221],"demonstrate":[222],"effectiveness":[224],"efficiency":[226],"proposed":[229],"methods":[230],"various":[232],"scenarios.":[234]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":20},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-15T08:11:43.952461","created_date":"2025-10-10T00:00:00"}
