{"id":"https://openalex.org/W4382202907","doi":"https://doi.org/10.1609/aaai.v37i9.26232","title":"Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting It into MLPs: An Effective GNN-to-MLP Distillation Framework","display_name":"Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting It into MLPs: An Effective GNN-to-MLP Distillation Framework","publication_year":2023,"publication_date":"2023-06-26","ids":{"openalex":"https://openalex.org/W4382202907","doi":"https://doi.org/10.1609/aaai.v37i9.26232"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v37i9.26232","doi":"https://doi.org/10.1609/aaai.v37i9.26232","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v37i9.26232","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/26232/26004","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/26232/26004","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020499056","display_name":"Lirong Wu","orcid":"https://orcid.org/0000-0003-1307-9312","observed_orcids":["https://orcid.org/0000-0003-1307-9312"]},"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"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lirong Wu","raw_affiliation_strings":["Westlake University\nZhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Westlake University\nZhejiang University","institution_ids":["https://openalex.org/I3133055985","https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100308160","display_name":"Haitao Lin","orcid":"https://orcid.org/0009-0003-7627-3718","observed_orcids":["https://orcid.org/0009-0003-7627-3718"]},"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"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haitao Lin","raw_affiliation_strings":["Westlake university\nZhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Westlake university\nZhejiang University","institution_ids":["https://openalex.org/I3133055985","https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103140108","display_name":"Yufei Huang","orcid":"https://orcid.org/0009-0007-8184-4529","observed_orcids":["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"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yufei Huang","raw_affiliation_strings":["Westlake University\nZhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Westlake University\nZhejiang University","institution_ids":["https://openalex.org/I3133055985","https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Tianyu Fan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyu Fan","raw_affiliation_strings":["Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082786719","display_name":"Stan Ziqing Li","orcid":"https://orcid.org/0000-0002-2961-8096","observed_orcids":["https://orcid.org/0000-0002-2961-8096"]},"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":"Stan Z. Li","raw_affiliation_strings":["Westlake University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Westlake University","institution_ids":["https://openalex.org/I3133055985"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.6567,"has_fulltext":true,"cited_by_count":32,"citation_normalized_percentile":{"value":0.97060005,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"37","issue":"9","first_page":"10351","last_page":"10360"},"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/T12292","display_name":"Graph Theory and Algorithms","score":9.999999747378752e-05,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/knowledge-distillation","display_name":"knowledge distillation","score":0.9660000205039978},{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.9629999995231628},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":0.890999972820282},{"id":"https://openalex.org/keywords/spectral-domain","display_name":"spectral domain","score":0.5789999961853027},{"id":"https://openalex.org/keywords/spatial-domain","display_name":"spatial domain","score":0.8109999895095825}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7250454425811768},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.6908165812492371},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5765172839164734},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5578476190567017},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5452067255973816},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5259568691253662},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5211989283561707},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.49630290269851685},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.4550929069519043},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4254000782966614},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35040879249572754},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34322693943977356},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.14563822746276855},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.06550469994544983},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0652940571308136},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1609/aaai.v37i9.26232","doi":"https://doi.org/10.1609/aaai.v37i9.26232","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v37i9.26232","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/26232/26004","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2305.10758","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.10758","pdf_url":"https://arxiv.org/pdf/2305.10758","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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:ojs.aaai.org:article/26232","doi":null,"is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/26232","pdf_url":null,"source":{"id":"https://openalex.org/S4306400155","display_name":"AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801652627","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/I2801652627"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"},{"id":"doi:10.48550/arxiv.2305.10758","doi":"https://doi.org/10.48550/arxiv.2305.10758","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2305.10758","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1609/aaai.v37i9.26232","doi":"https://doi.org/10.1609/aaai.v37i9.26232","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v37i9.26232","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/26232/26004","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G129913845","display_name":null,"funder_award_id":"No. U21A20427","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2275806244","display_name":null,"funder_award_id":"2021YFA1301603","funder_id":"https://openalex.org/F4320321540","funder_display_name":"Ministry of Science and Technology of the People's Republic of China"},{"id":"https://openalex.org/G2543416449","display_name":null,"funder_award_id":"U21A20427","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321540","display_name":"Ministry of Science and Technology of the People's Republic of China","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4382202907.pdf","grobid_xml":"https://content.openalex.org/works/W4382202907.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W1578099820","https://openalex.org/W1662382123","https://openalex.org/W1821462560","https://openalex.org/W1971368490","https://openalex.org/W2101491865","https://openalex.org/W2153959628","https://openalex.org/W2468907370","https://openalex.org/W2519887557","https://openalex.org/W2624431344","https://openalex.org/W2766453196","https://openalex.org/W2786672974","https://openalex.org/W2788512147","https://openalex.org/W2900470550","https://openalex.org/W2907492528","https://openalex.org/W2914721378","https://openalex.org/W2916106175","https://openalex.org/W2936604471","https://openalex.org/W2974767112","https://openalex.org/W2985881958","https://openalex.org/W2995509042","https://openalex.org/W2995983896","https://openalex.org/W3015858876","https://openalex.org/W3030204405","https://openalex.org/W3034368386","https://openalex.org/W3042770487","https://openalex.org/W3080083111","https://openalex.org/W3080555959","https://openalex.org/W3081191522","https://openalex.org/W3090016979","https://openalex.org/W3093814892","https://openalex.org/W3128443161","https://openalex.org/W3146371520","https://openalex.org/W3152626252","https://openalex.org/W3152893301","https://openalex.org/W3162142949","https://openalex.org/W3175063760","https://openalex.org/W3187295906","https://openalex.org/W3207031492","https://openalex.org/W3212385273","https://openalex.org/W3215873837","https://openalex.org/W4210257598","https://openalex.org/W4210386866","https://openalex.org/W4221023051","https://openalex.org/W4287754915","https://openalex.org/W4306317081","https://openalex.org/W6603850445"],"related_works":["https://openalex.org/W4380353743","https://openalex.org/W4403577472","https://openalex.org/W7221040046","https://openalex.org/W7138294406","https://openalex.org/W7124144102","https://openalex.org/W4361192749","https://openalex.org/W7154503705","https://openalex.org/W7138312950","https://openalex.org/W4392490472","https://openalex.org/W4414360645"],"abstract_inverted_index":{"Recent":[0],"years":[1],"have":[2,148],"witnessed":[3],"the":[4,19,59,95,106,115,131,135,142,201],"great":[5],"success":[6],"of":[7,61,67,73,134],"Graph":[8],"Neural":[9],"Networks":[10],"(GNNs)":[11],"in":[12,105,114,150],"handling":[13],"graph-related":[14],"tasks.":[15],"However,":[16,58],"MLPs":[17,203],"remain":[18],"primary":[20],"workhorse":[21],"for":[22,126],"practical":[23],"industrial":[24],"applications":[25],"due":[26],"to":[27,48,78,164],"their":[28,36,112],"desirable":[29],"inference":[30],"efficiency":[31],"and":[32,69,81,102,109,162,184,189,206,219],"scalability.":[33],"To":[34],"reduce":[35],"gaps,":[37],"one":[38],"can":[39],"directly":[40],"distill":[41],"knowledge":[42,71,96,133,144,186],"from":[43,187],"a":[44,49,65,121],"well-designed":[45],"teacher":[46,210],"GNN":[47,222],"student":[50],"MLP,":[51],"which":[52,180],"is":[53],"termed":[54],"as":[55],"GNN-to-MLP":[56,128,176],"distillation.":[57],"process":[60],"distillation":[62,178],"usually":[63],"entails":[64],"loss":[66],"information,":[68],"``which":[70],"patterns":[72],"GNNs":[74,99,137,188,211],"are":[75],"more":[76],"likely":[77],"be":[79,139],"left":[80],"distilled":[82],"into":[83,100,192],"MLPs?\"":[84],"becomes":[85],"an":[86,173],"important":[87],"question.":[88],"In":[89,168],"this":[90,169],"paper,":[91,170],"we":[92,119,147,171],"first":[93],"factorize":[94],"learned":[97],"by":[98,141,204,212],"low-":[101],"high-frequency":[103,132,185],"components":[104],"spectral":[107],"domain":[108],"then":[110],"derive":[111],"correspondence":[113],"spatial":[116],"domain.":[117],"Furthermore,":[118],"identified":[120],"potential":[122],"information":[123],"drowning":[124],"problem":[125],"existing":[127],"distillation,":[129],"i.e.,":[130],"pre-trained":[136],"may":[138],"overwhelmed":[140],"low-frequency":[143,183],"during":[145],"distillation;":[146],"described":[149],"detail":[151],"what":[152,158],"it":[153,156,160,191],"represents,":[154],"how":[155,163],"arises,":[157],"impact":[159],"has,":[161],"deal":[165],"with":[166],"it.":[167],"propose":[172],"efficient":[174],"Full-Frequency":[175],"(FF-G2M)":[177],"framework,":[179],"extracts":[181],"both":[182],"injects":[190],"MLPs.":[193],"Extensive":[194],"experiments":[195],"show":[196],"that":[197],"FF-G2M":[198],"improves":[199],"over":[200,215],"vanilla":[202],"12.6%":[205],"outperforms":[207],"its":[208],"corresponding":[209],"2.6%":[213],"averaged":[214],"six":[216],"graph":[217],"datasets":[218],"three":[220],"common":[221],"architectures.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":1}],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2023-06-28T00:00:00"}