{"id":"https://openalex.org/W7154330828","doi":"https://doi.org/10.48550/arxiv.2604.11473","title":"Learning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification","display_name":"Learning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154330828","doi":"https://doi.org/10.48550/arxiv.2604.11473"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11473","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11473","pdf_url":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.11473","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133577941","display_name":"Jiajun Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Jiajun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133579414","display_name":"Yadong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yadong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032298471","display_name":"Xuanze Chen","orcid":"https://orcid.org/0000-0002-0304-2391"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xuanze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133626505","display_name":"Chen Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133594210","display_name":"Chuang Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Chuang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133611314","display_name":"Shanqing Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Shanqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133570126","display_name":"Qi Xuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xuan, Qi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9733999967575073,"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":0.9733999967575073,"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":0.0044999998062849045,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.001500000013038516,"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/discriminative-model","display_name":"Discriminative model","score":0.6575999855995178},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6486999988555908},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4912000000476837},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4607999920845032},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.43650001287460327},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4307999908924103},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.3707999885082245},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.3571000099182129},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.35569998621940613}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8036999702453613},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6575999855995178},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6486999988555908},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5547000169754028},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4912000000476837},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4862000048160553},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4607999920845032},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.43650001287460327},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3961000144481659},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.3707999885082245},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.3571000099182129},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.35569998621940613},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.3427000045776367},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33169999718666077},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.326200008392334},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C2777480716","wikidata":"https://www.wikidata.org/wiki/Q23582796","display_name":"Resource consumption","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26669999957084656},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11473","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11473","pdf_url":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.11473","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11473","pdf_url":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.7346922755241394,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Mixture-of-Experts":[0],"(MoE)":[1],"architectures":[2],"offer":[3],"a":[4,27,67,88,95],"scalable":[5],"path":[6],"for":[7,51,57,91,111,121],"Graph":[8,168],"Neural":[9],"Networks":[10],"(GNNs)":[11],"in":[12,142],"node":[13,122],"classification":[14],"tasks":[15],"but":[16],"typically":[17],"rely":[18],"on":[19,35,105,125,144,148],"static":[20,75],"and":[21,47,54,116,159],"rigid":[22],"routing":[23,98],"strategies":[24],"that":[25,70,129],"enforce":[26],"uniform":[28],"expert":[29,33,76,80,103,118],"budget":[30,119],"or":[31],"coarse-grained":[32],"toggles":[34],"all":[36],"nodes.":[37],"This":[38],"limitation":[39],"overlooks":[40],"the":[41,72,166],"varying":[42],"discriminative":[43],"difficulty":[44],"of":[45],"nodes":[46,53,107],"leads":[48],"to":[49,78,100,140,157,165],"under-fitting":[50],"hard":[52,106],"redundant":[55],"computation":[56],"easy":[58,112],"ones.":[59],"To":[60],"resolve":[61],"this":[62],"issue,":[63],"we":[64],"propose":[65],"D2MoE,":[66],"novel":[68],"framework":[69],"shifts":[71],"focus":[73],"from":[74],"selection":[77],"node-wise":[79],"resource":[81],"allocation.":[82],"By":[83],"using":[84],"predictive":[85],"entropy":[86],"as":[87],"real-time":[89],"proxy":[90],"difficulty,":[92],"D2MoE":[93,130],"employs":[94],"difficulty-driven":[96],"top-p":[97],"mechanism":[99],"adaptively":[101],"concentrate":[102],"resources":[104],"while":[108],"reducing":[109],"overhead":[110],"ones,":[113],"achieving":[114],"continuous":[115],"fine-grained":[117],"scaling":[120],"classification.":[123],"Experiments":[124],"13":[126],"benchmarks":[127],"demonstrate":[128],"achieves":[131],"consistent":[132],"state-of-the-art":[133],"performance,":[134],"surpassing":[135],"leading":[136],"baselines":[137],"by":[138,155,162],"up":[139,156],"7.92%":[141],"accuracy":[143],"heterophilous":[145],"graphs.":[146],"Notably,":[147],"large-scale":[149],"graphs,":[150],"it":[151],"reduces":[152],"memory":[153],"consumption":[154],"73.07%":[158],"training":[160],"time":[161],"46.53%":[163],"compared":[164],"best-performing":[167],"MoE,":[169],"thereby":[170],"validating":[171],"its":[172],"superior":[173],"efficiency.":[174]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-15T00:00:00"}
