{"id":"https://openalex.org/W4414360645","doi":"https://doi.org/10.24963/ijcai.2025/309","title":"Suit the Node Pair to the Case: A Multi-Scale Node Pair Grouping Strategy for Graph-MLP Distillation","display_name":"Suit the Node Pair to the Case: A Multi-Scale Node Pair Grouping Strategy for Graph-MLP Distillation","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414360645","doi":"https://doi.org/10.24963/ijcai.2025/309"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/309","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/309","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","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/A5031568775","display_name":"Rui Dong","orcid":"https://orcid.org/0000-0002-7143-5007"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rui Dong","raw_affiliation_strings":["Southeast University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100636528","display_name":"Jiaxing Li","orcid":"https://orcid.org/0000-0001-7048-9284"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiaxing Li","raw_affiliation_strings":["Southeast University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101373623","display_name":"Weihuang Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weihuang Zheng","raw_affiliation_strings":["Southeast University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008751186","display_name":"Youyong Kong","orcid":"https://orcid.org/0000-0003-2095-8470"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Youyong Kong","raw_affiliation_strings":["Southeast University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University","institution_ids":[]}]}],"institutions":[],"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":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2775","last_page":"2783"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10791","display_name":"Advanced Control Systems Optimization","score":0.7645000219345093,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10791","display_name":"Advanced Control Systems Optimization","score":0.7645000219345093,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.7336000204086304,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12288","display_name":"Optimization and Search Problems","score":0.6690000295639038,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/node","display_name":"Node (physics)","score":0.7289000153541565},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6575000286102295},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6291000247001648},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.5871000289916992},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.439300000667572},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3970000147819519},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.3571999967098236}],"concepts":[{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.7289000153541565},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7069000005722046},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6575000286102295},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6291000247001648},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.5871000289916992},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46779999136924744},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41370001435279846},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3970000147819519},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.3571999967098236},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30489999055862427},{"id":"https://openalex.org/C84685590","wikidata":"https://www.wikidata.org/wiki/Q1540472","display_name":"Knowledge engineering","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2727000117301941},{"id":"https://openalex.org/C154030694","wikidata":"https://www.wikidata.org/wiki/Q1436074","display_name":"Fractionating column","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2630000114440918},{"id":"https://openalex.org/C176032523","wikidata":"https://www.wikidata.org/wiki/Q753127","display_name":"Topological sorting","level":3,"score":0.2590999901294708},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/309","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/309","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Network":[2],"(GNN)":[3],"is":[4,40,180],"powerful":[5],"in":[6,69,81,94,168],"solving":[7],"various":[8],"graph-related":[9],"tasks,":[10],"while":[11,52],"its":[12],"message":[13],"passing":[14],"mechanism":[15],"may":[16],"lead":[17],"to":[18,36,64,106,112,122,126,130],"latency":[19],"during":[20],"inference":[21,28],"time.":[22],"Multi-Layer-Perceptron":[23],"(MLP)":[24],"can":[25,164],"achieve":[26],"fast":[27],"speed":[29],"but":[30],"with":[31],"limited":[32],"performance.":[33],"One":[34],"solution":[35],"fill":[37],"this":[38,95],"gap":[39],"through":[41],"Knowledge":[42],"Distillation.":[43],"However,":[44],"current":[45,72],"distillation":[46,62,102,161],"methods":[47,73],"follow":[48],"a":[49,99,117,159],"''node-to-node''":[50],"paradigm,":[51],"considering":[53],"the":[54,77,82,146,153,169,183,193],"complex":[55],"relationships":[56],"between":[57],"different":[58],"node":[59,124,131,140,148],"pairs,":[60],"direct":[61],"fails":[63],"capture":[65,107,165],"these":[66],"multiple-granularity":[67],"features":[68,141],"GNN.":[70],"Furthermore,":[71],"which":[74],"focuses":[75],"on":[76,152,188],"alignment":[78],"of":[79,145,177,195],"logits":[80],"final":[83,184],"layer":[84,179],"ignores":[85],"further":[86],"learning":[87],"within":[88],"layers":[89],"inside":[90],"student":[91],"MLP.":[92,113],"Therefore,":[93],"paper,":[96],"we":[97,157],"introduce":[98],"multi-scale":[100,118,160],"knowledge":[101,109,167],"method":[103,162],"(MSN-GDM)":[104],"aiming":[105],"multiple":[108],"from":[110],"GNN":[111],"We":[114],"firstly":[115],"propose":[116],"node-pair":[119],"grouping":[120],"strategy":[121],"assign":[123],"pairs":[125],"different-scale":[127],"groups":[128],"according":[129],"pair":[132],"similarity":[133,136],"metrics.":[134],"The":[135,173],"metrics":[137],"consider":[138],"both":[139],"and":[142],"topological":[143],"structures":[144],"given":[147],"pair.":[149],"Then":[150],"based":[151],"preprocessed":[154],"node-set":[155,171],"groups,":[156],"design":[158],"that":[163],"comprehensive":[166],"corresponding":[170],"groups.":[172],"hierarchical":[174],"weighted":[175],"sum":[176],"each":[178],"applied":[181],"as":[182],"output.":[185],"Extensive":[186],"experiments":[187],"eight":[189],"real-world":[190],"datasets":[191],"demonstrate":[192],"effectiveness":[194],"our":[196],"proposed":[197],"method.":[198]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
