{"id":"https://openalex.org/W4408354052","doi":"https://doi.org/10.1109/icassp49660.2025.10888559","title":"Advanced Graph-MLPs Distillation based on Global and Local Hyperbolic Geometry Learning","display_name":"Advanced Graph-MLPs Distillation based on Global and Local Hyperbolic Geometry Learning","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408354052","doi":"https://doi.org/10.1109/icassp49660.2025.10888559"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10888559","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10888559","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5028716128","display_name":"Yao Guan","orcid":"https://orcid.org/0000-0002-8680-1866"},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Guan","raw_affiliation_strings":["Nanjing Normal University,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing Normal University,Nanjing,China","institution_ids":["https://openalex.org/I152031979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033299469","display_name":"Wenzhu Yan","orcid":"https://orcid.org/0000-0003-1560-693X"},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenzhu Yan","raw_affiliation_strings":["Nanjing Normal University,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing Normal University,Nanjing,China","institution_ids":["https://openalex.org/I152031979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082192854","display_name":"Ting Yuan","orcid":"https://orcid.org/0000-0001-6997-2113"},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Yuan","raw_affiliation_strings":["Nanjing Normal University,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing Normal University,Nanjing,China","institution_ids":["https://openalex.org/I152031979"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109598917","display_name":"Yanmeng Li","orcid":null},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanmeng Li","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,Nanjing,China","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04128485,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13717","display_name":"Advanced Algorithms and Applications","score":0.8596000075340271,"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/T13717","display_name":"Advanced Algorithms and Applications","score":0.8596000075340271,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.8090000152587891,"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/T10320","display_name":"Neural Networks and Applications","score":0.722100019454956,"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":0.6046998500823975},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.5477204322814941},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.48328712582588196},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.38083475828170776},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3773869574069977},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3263397812843323},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.30018413066864014},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24426284432411194},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.06546586751937866}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6046998500823975},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.5477204322814941},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.48328712582588196},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.38083475828170776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3773869574069977},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3263397812843323},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.30018413066864014},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24426284432411194},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.06546586751937866},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10888559","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10888559","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2116341502","https://openalex.org/W2153959628","https://openalex.org/W2797520557","https://openalex.org/W2907492528","https://openalex.org/W3152626252","https://openalex.org/W4224927203","https://openalex.org/W4231449374","https://openalex.org/W4235484205","https://openalex.org/W4286564630","https://openalex.org/W4392902848","https://openalex.org/W6638523607","https://openalex.org/W6726873649","https://openalex.org/W6751693566","https://openalex.org/W6766952790","https://openalex.org/W6767121309","https://openalex.org/W6784944647","https://openalex.org/W6802881785","https://openalex.org/W6851764788","https://openalex.org/W6856418394"],"related_works":["https://openalex.org/W3026162553","https://openalex.org/W2344382886","https://openalex.org/W19111321","https://openalex.org/W2412887479","https://openalex.org/W32245304","https://openalex.org/W2953684491","https://openalex.org/W4285338581","https://openalex.org/W2768175398","https://openalex.org/W2015158429","https://openalex.org/W2080290893"],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"have":[4],"drawn":[5],"great":[6],"research":[7],"attention":[8],"for":[9,20,57,70,90],"graph":[10,14,71,92,129,168,173,187],"machine":[11],"learning.":[12,116,196],"However,":[13],"learning":[15],"techniques":[16],"are":[17,54],"extremely":[18],"difficult":[19],"practical":[21],"deployments":[22],"in":[23,59,97],"the":[24,29,42,66,127,139,146,157,172,177,182],"industry":[25],"applications":[26],"owing":[27],"to":[28,40,124,170],"scalability":[30],"challenges":[31],"incurred":[32],"by":[33,212],"data":[34,93,169],"dependency.":[35],"Although":[36],"some":[37],"works":[38],"attempt":[39],"establish":[41],"known":[43],"efficient":[44],"MLPs":[45,211],"with":[46,94,130,205],"GNNs":[47,206,220],"based":[48,109,134,189],"on":[49,110,135,190,214],"knowledge":[50,136,142,160],"distillation":[51],"(KD),":[52],"they":[53],"primarily":[55],"designed":[56],"graphs":[58,77],"Euclidean":[60,82,151],"spaces,":[61],"which":[62],"can":[63,144,162],"not":[64],"provide":[65],"most":[67],"powerful":[68],"geometry":[69,115,195],"representation":[72,174],"as":[73],"numerous":[74],"real-":[75],"world":[76],"display":[78],"a":[79,102],"combination":[80],"of":[81,120,186],"and":[83,112,153,192,207],"Hyperbolic":[84,154],"geometry.":[85],"To":[86],"achieve":[87],"comprehensive":[88],"expression":[89],"complex":[91,128],"high":[95,183],"efficiency,":[96],"this":[98],"paper,":[99],"we":[100],"proposed":[101],"novel":[103],"Advanced":[104],"Graph-MLPs":[105],"Distillation":[106],"Framework":[107],"(AGMDF)":[108],"global":[111,140,191],"local":[113,158,193],"hyperbolic":[114,132,159,194],"The":[117],"key":[118],"point":[119],"our":[121],"method":[122],"is":[123],"fully":[125],"exploit":[126,145],"additional":[131],"properties":[133],"distillation.":[137],"Specifically,":[138],"cross-geometric":[141],"fusion":[143],"compensation":[147],"information":[148],"learned":[149],"from":[150],"view":[152],"domain.":[155],"Then,":[156],"enhancement":[161],"employ":[163],"prominent":[164],"tree-likeness":[165],"components":[166],"among":[167],"improve":[171],"ability.":[175],"Thus,":[176],"distilled":[178],"MLP":[179],"model":[180],"enjoys":[181],"expressive":[184],"ability":[185],"context-awareness":[188],"Extensive":[197],"experiments":[198],"show":[199],"that":[200],"AGMDF":[201],"achieves":[202],"competitive":[203],"accuracy":[204],"improves":[208],"over":[209],"stand-alone":[210],"21.84%":[213],"average":[215],"while":[216],"inferring":[217],"faster":[218],"than":[219],"across":[221],"five":[222],"benchmark":[223],"datasets.":[224]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}