{"id":"https://openalex.org/W4403577472","doi":"https://doi.org/10.1145/3627673.3679586","title":"Teach Harder, Learn Poorer: Rethinking Hard Sample Distillation for GNN-to-MLP Knowledge Distillation","display_name":"Teach Harder, Learn Poorer: Rethinking Hard Sample Distillation for GNN-to-MLP Knowledge Distillation","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4403577472","doi":"https://doi.org/10.1145/3627673.3679586"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3679586","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679586","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3627673.3679586","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041519364","display_name":"Lirong Wu","orcid":"https://orcid.org/0000-0001-5551-3194"},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lirong Wu","raw_affiliation_strings":["Westlake University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-5551-3194","affiliations":[{"raw_affiliation_string":"Westlake University, Hangzhou, China","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yunfan Liu","orcid":"https://orcid.org/0009-0002-1639-5855"},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunfan Liu","raw_affiliation_strings":["Westlake University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0002-1639-5855","affiliations":[{"raw_affiliation_string":"Westlake University, Hangzhou, China","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115599158","display_name":"Haitao Lin","orcid":"https://orcid.org/0000-0002-3153-7746"},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haitao Lin","raw_affiliation_strings":["Westlake University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-3153-7746","affiliations":[{"raw_affiliation_string":"Westlake University, Hangzhou, China","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103140108","display_name":"Yufei Huang","orcid":"https://orcid.org/0009-0007-8184-4529"},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yufei Huang","raw_affiliation_strings":["Westlake University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0007-8184-4529","affiliations":[{"raw_affiliation_string":"Westlake University, Hangzhou, China","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082786719","display_name":"Stan Z. Li","orcid":"https://orcid.org/0000-0002-2961-8096"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stan Z. Li","raw_affiliation_strings":["Westlake University, Hangzhou, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-2961-8096","affiliations":[{"raw_affiliation_string":"Westlake University, Hangzhou, Singapore","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0648,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.7956688,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2554","last_page":"2563"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9894999861717224,"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/T10028","display_name":"Topic Modeling","score":0.9894999861717224,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9868999719619751,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9797999858856201,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.906476616859436},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6290789842605591},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5963146686553955},{"id":"https://openalex.org/keywords/process-engineering","display_name":"Process engineering","score":0.4321642518043518},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3594047725200653},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3568269610404968},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.2596355080604553},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16177818179130554},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.13331744074821472}],"concepts":[{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.906476616859436},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6290789842605591},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5963146686553955},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.4321642518043518},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3594047725200653},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3568269610404968},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.2596355080604553},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16177818179130554},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.13331744074821472}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627673.3679586","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679586","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3627673.3679586","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679586","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","display_name":"No poverty","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2162630660","https://openalex.org/W2168190036","https://openalex.org/W2907492528","https://openalex.org/W2997006708","https://openalex.org/W2998496395","https://openalex.org/W3015858876","https://openalex.org/W3030204405","https://openalex.org/W3034795332","https://openalex.org/W3080083111","https://openalex.org/W3152626252","https://openalex.org/W3152893301","https://openalex.org/W3203532272","https://openalex.org/W3204453541","https://openalex.org/W3204703845","https://openalex.org/W4281560842","https://openalex.org/W4282970521","https://openalex.org/W4287121309","https://openalex.org/W4320060387","https://openalex.org/W4366463135","https://openalex.org/W4382202907","https://openalex.org/W4392979548","https://openalex.org/W6796782416"],"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":{"To":[0],"bridge":[1],"the":[2,37,45,80,87,93,101,131,161,179,188,194],"gaps":[3],"between":[4],"powerful":[5],"Graph":[6],"Neural":[7],"Networks":[8],"(GNNs)":[9],"and":[10,49,86,134,148,199],"lightweight":[11],"Multi-Layer":[12],"Perceptron":[13],"(MLPs),":[14],"GNN-to-MLP":[15,67],"Knowledge":[16],"Distillation":[17],"(KD)":[18],"proposes":[19,119],"to":[20,153],"distill":[21,154],"knowledge":[22,38,77,156],"from":[23,44,157],"a":[24,29,57,120,138],"well-trained":[25],"teacher":[26,42,158,202],"GNN":[27,84],"into":[28,160],"student":[30,165,180],"MLP.":[31],"In":[32],"this":[33],"paper,":[34],"we":[35],"revisit":[36],"samples":[39],"(nodes)":[40],"in":[41],"GNNs":[43,159,203],"perspective":[46],"of":[47,61,73,83,95,100,108,164,187],"hardness,":[48,74],"identify":[50],"that":[51],"hard":[52],"sample":[53],"distillation":[54,90,144],"may":[55],"be":[56,213],"major":[58],"performance":[59],"bottleneck":[60],"existing":[62,102],"graph":[63],"KD":[64,68],"algorithms.":[65],"The":[66],"involves":[69],"two":[70,132,142],"different":[71],"types":[72],"one":[75,107,115],"student-free":[76],"hardness":[78,91],"describing":[79,92],"inherent":[81],"complexity":[82],"knowledge,":[85],"other":[88],"student-dependent":[89],"difficulty":[94],"teacher-to-student":[96],"distillation.":[97],"However,":[98],"most":[99,186],"work":[103],"focuses":[104],"on":[105],"only":[106],"these":[109],"aspects":[110],"or":[111],"regards":[112],"them":[113,136],"as":[114],"thing.":[116],"This":[117],"paper":[118],"simple":[121],"yet":[122],"effective":[123],"<u>H</u>ardness-aware":[124],"<u>G</u>NN-to-<u>M</u>LP":[125],"<u>D</u>istillation":[126],"(HGMD)":[127],"framework,":[128],"which":[129],"decouples":[130],"hardnesses":[133],"estimates":[135],"using":[137],"non-parametric":[139,168],"approach.":[140],"Finally,":[141],"hardness-aware":[143,155],"schemes":[145],"(i.e.,":[146],"HGMD-weight":[147],"HGMD-mixup)":[149],"are":[150],"further":[151],"proposed":[152],"corresponding":[162],"nodes":[163],"MLPs.":[166],"As":[167],"distillation,":[169],"HGMD":[170],"does":[171],"not":[172],"involve":[173],"any":[174],"additional":[175],"learnable":[176],"parameters":[177],"beyond":[178],"MLPs,":[181],"but":[182],"it":[183],"still":[184],"outperforms":[185,200],"state-of-the-art":[189],"competitors.":[190],"HGMD-mixup":[191],"improves":[192],"over":[193,207],"vanilla":[195],"MLPs":[196],"by":[197,204],"12.95%":[198],"its":[201],"2.48%":[205],"averaged":[206],"seven":[208],"real-world":[209],"datasets.":[210],"Codes":[211],"will":[212],"made":[214],"public":[215],"at":[216],"https://github.com/LirongWu/HGMD.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}