{"id":"https://openalex.org/W4385565129","doi":"https://doi.org/10.1145/3580305.3599362","title":"Generalizing Graph ODE for Learning Complex System Dynamics across Environments","display_name":"Generalizing Graph ODE for Learning Complex System Dynamics across Environments","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385565129","doi":"https://doi.org/10.1145/3580305.3599362"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599362","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599362","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599362","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599362","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069765205","display_name":"Zijie Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zijie Huang","raw_affiliation_strings":["University of California, Los Angeles, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025213473","display_name":"Yizhou Sun","orcid":"https://orcid.org/0000-0003-1812-6843"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yizhou Sun","raw_affiliation_strings":["University of California, Los Angeles, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100392089","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-8180-2886"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["University of California, Los Angeles, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5069765205"],"corresponding_institution_ids":["https://openalex.org/I161318765"],"apc_list":null,"apc_paid":null,"fwci":2.7653,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.92203269,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"798","last_page":"809"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9836999773979187,"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":0.9836999773979187,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.978600025177002,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9753999710083008,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/ode","display_name":"Ode","score":0.9143904447555542},{"id":"https://openalex.org/keywords/ordinary-differential-equation","display_name":"Ordinary differential equation","score":0.7057147026062012},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6297863721847534},{"id":"https://openalex.org/keywords/parameterized-complexity","display_name":"Parameterized complexity","score":0.6019973754882812},{"id":"https://openalex.org/keywords/system-dynamics","display_name":"System dynamics","score":0.5257095694541931},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4776574373245239},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4482812285423279},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42438963055610657},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.40556347370147705},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4007335603237152},{"id":"https://openalex.org/keywords/differential-equation","display_name":"Differential equation","score":0.3471231162548065},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21478602290153503},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.1783682405948639},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.17048370838165283}],"concepts":[{"id":"https://openalex.org/C34862557","wikidata":"https://www.wikidata.org/wiki/Q178985","display_name":"Ode","level":2,"score":0.9143904447555542},{"id":"https://openalex.org/C51544822","wikidata":"https://www.wikidata.org/wiki/Q465274","display_name":"Ordinary differential equation","level":3,"score":0.7057147026062012},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6297863721847534},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.6019973754882812},{"id":"https://openalex.org/C77405623","wikidata":"https://www.wikidata.org/wiki/Q598451","display_name":"System dynamics","level":2,"score":0.5257095694541931},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4776574373245239},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4482812285423279},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42438963055610657},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40556347370147705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4007335603237152},{"id":"https://openalex.org/C78045399","wikidata":"https://www.wikidata.org/wiki/Q11214","display_name":"Differential equation","level":2,"score":0.3471231162548065},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21478602290153503},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.1783682405948639},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.17048370838165283},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599362","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599362","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599362","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3580305.3599362","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599362","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599362","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1063757644","display_name":null,"funder_award_id":"2303037","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2636502100","display_name":"CSEDI Collaborative Research: Paleomagnetism, the Geodynamo, and Core-Mantle Interactions","funder_award_id":"0112290","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2901143931","display_name":null,"funder_award_id":"2106859","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7066469448","display_name":null,"funder_award_id":"2119643","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7189626769","display_name":null,"funder_award_id":"1829071","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7353082873","display_name":"PIPP Phase I: An End-to-End Pandemic Early Warning System by Harnessing Open-source Intelligence","funder_award_id":"2200274","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8659250076","display_name":null,"funder_award_id":"2211557","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"},{"id":"https://openalex.org/F4320307791","display_name":"Cisco Systems","ror":"https://ror.org/03yt1ez60"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385565129.pdf","grobid_xml":"https://content.openalex.org/works/W4385565129.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1984965649","https://openalex.org/W2110238570","https://openalex.org/W2134066908","https://openalex.org/W2534928453","https://openalex.org/W2560674852","https://openalex.org/W2765076328","https://openalex.org/W2912251486","https://openalex.org/W2945827670","https://openalex.org/W2967246123","https://openalex.org/W2998116985","https://openalex.org/W3012644407","https://openalex.org/W3012727582","https://openalex.org/W3012871709","https://openalex.org/W3045200674","https://openalex.org/W3080690551","https://openalex.org/W3100278010","https://openalex.org/W3100679886","https://openalex.org/W3134509497","https://openalex.org/W3170848208","https://openalex.org/W3190208102","https://openalex.org/W4241888870","https://openalex.org/W4287126984","https://openalex.org/W4312549509"],"related_works":["https://openalex.org/W3082668976","https://openalex.org/W2517973022","https://openalex.org/W1543121148","https://openalex.org/W1580703421","https://openalex.org/W2618518959","https://openalex.org/W2231364979","https://openalex.org/W4295745414","https://openalex.org/W4382202915","https://openalex.org/W4288093367","https://openalex.org/W2489516236"],"abstract_inverted_index":{"Learning":[0],"multi-agent":[1,129],"system":[2,19,39,130,137,246,262],"dynamics":[3,15,25,44,98,131,138,166],"have":[4],"been":[5],"extensively":[6],"studied":[7],"for":[8,126,192,203],"various":[9,252],"real-world":[10],"applications,":[11],"such":[12,74],"as":[13,75],"molecular":[14],"in":[16,21,26,70,265],"biology,":[17],"multi-body":[18],"prediction":[20,104],"physics,":[22],"and":[23,49,77,101,226,233,269],"particle":[24],"material":[27],"science.":[28],"Most":[29],"of":[30,239],"the":[31,43,51,93,97,153,160,165,198,219,222,236,244,266],"existing":[32],"models":[33],"are":[34,63,170,195],"built":[35],"to":[36,83,91,151,201,216,273],"learn":[37,42,84],"single":[38],"dynamics,":[40,263],"which":[41,68],"from":[45],"observed":[46],"historical":[47],"data":[48,108],"predict":[50,261],"future":[52],"trajectory.":[53],"In":[54],"practice,":[55],"however,":[56],"we":[57,114,210],"might":[58],"observe":[59],"multiple":[60,85],"systems":[61,275],"that":[62,176,256],"generated":[64],"across":[65,99,132,167],"different":[66,168],"environments,":[67],"differ":[69],"latent":[71,188],"exogenous":[72,189,227,241],"factors":[73,190,228,242],"temperature":[76],"gravity.":[78],"One":[79],"simple":[80],"solution":[81],"is":[82,109],"environment-specific":[86],"models,":[87],"but":[88],"it":[89],"fails":[90],"exploit":[92],"potential":[94],"commonalities":[95],"among":[96,156],"environments":[100,169],"offers":[102],"poor":[103],"results":[105],"where":[106],"per-environment":[107],"sparse":[110],"or":[111],"limited.":[112],"Here,":[113],"present":[115],"GG-ODE":[116],"(Generalized":[117],"Graph":[118,147],"Ordinary":[119],"Differential":[120],"Equations),":[121],"a":[122,182],"machine":[123],"learning":[124,127,181],"framework":[125],"continuous":[128,154],"environments.":[133],"Our":[134],"model":[135,161,208,258],"learns":[136],"using":[139],"neural":[140],"ordinary":[141],"differential":[142],"equations":[143],"(ODE)":[144],"parameterized":[145],"by":[146,163,172],"Neural":[148],"Networks":[149],"(GNNs)":[150],"capture":[152],"interaction":[155],"agents.":[157],"We":[158],"achieve":[159],"generalization":[162],"assuming":[164],"governed":[171],"common":[173],"physics":[174],"laws":[175],"can":[177,259,270],"be":[178],"captured":[179],"via":[180,229,247],"shared":[183],"ODE":[184,199],"function.":[185],"The":[186],"distinct":[187],"learned":[191,223,240],"each":[193],"environment":[194],"incorporated":[196],"into":[197],"function":[200],"account":[202],"their":[204],"differences.":[205],"To":[206],"improve":[207],"performance,":[209],"additionally":[211],"design":[212],"two":[213],"regularization":[214],"losses":[215],"(1)":[217],"enforce":[218],"orthogonality":[220],"between":[221],"initial":[224],"states":[225],"mutual":[230],"information":[231],"minimization;":[232],"(2)":[234],"reduce":[235],"temporal":[237],"variance":[238],"within":[243],"same":[245],"contrastive":[248],"learning.":[249],"Experiments":[250],"over":[251],"physical":[253],"simulations":[254],"show":[255],"our":[257],"accurately":[260],"especially":[264],"long":[267],"range,":[268],"generalize":[271],"well":[272],"new":[274],"with":[276],"few":[277],"observations.":[278]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}