{"id":"https://openalex.org/W2949989211","doi":"https://doi.org/10.1145/3394486.3403132","title":"Neural Dynamics on Complex Networks","display_name":"Neural Dynamics on Complex Networks","publication_year":2020,"publication_date":"2020-08-20","ids":{"openalex":"https://openalex.org/W2949989211","doi":"https://doi.org/10.1145/3394486.3403132","mag":"2949989211"},"language":"en","primary_location":{"id":"doi:10.1145/3394486.3403132","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403132","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403132","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403132","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074254110","display_name":"Chengxi Zang","orcid":"https://orcid.org/0000-0002-8244-9551"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]},{"id":"https://openalex.org/I4387153466","display_name":"Weill Cornell Medicine","ror":"https://ror.org/02r109517","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295","https://openalex.org/I4387153466"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chengxi Zang","raw_affiliation_strings":["Weill Cornell Medicine, NYC, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Weill Cornell Medicine, NYC, NY, USA","institution_ids":["https://openalex.org/I205783295","https://openalex.org/I4387153466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100455768","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0001-9459-9461"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]},{"id":"https://openalex.org/I4387153466","display_name":"Weill Cornell Medicine","ror":"https://ror.org/02r109517","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295","https://openalex.org/I4387153466"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Wang","raw_affiliation_strings":["Weill Cornell Medicine, NYC, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Weill Cornell Medicine, NYC, NY, USA","institution_ids":["https://openalex.org/I205783295","https://openalex.org/I4387153466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9816,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.70857349,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"892","last_page":"902"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9851999878883362,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9789000153541565,"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":0.7289051413536072},{"id":"https://openalex.org/keywords/snapshot","display_name":"Snapshot (computer storage)","score":0.6799812912940979},{"id":"https://openalex.org/keywords/ordinary-differential-equation","display_name":"Ordinary differential equation","score":0.643500804901123},{"id":"https://openalex.org/keywords/ode","display_name":"Ode","score":0.5702925324440002},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5130175948143005},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.47372570633888245},{"id":"https://openalex.org/keywords/system-dynamics","display_name":"System dynamics","score":0.46544063091278076},{"id":"https://openalex.org/keywords/complex-system","display_name":"Complex system","score":0.4260403513908386},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4225144386291504},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41179609298706055},{"id":"https://openalex.org/keywords/differential-equation","display_name":"Differential equation","score":0.345746785402298},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3447956442832947},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15890321135520935},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.09548351168632507}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7289051413536072},{"id":"https://openalex.org/C55282118","wikidata":"https://www.wikidata.org/wiki/Q252683","display_name":"Snapshot (computer storage)","level":2,"score":0.6799812912940979},{"id":"https://openalex.org/C51544822","wikidata":"https://www.wikidata.org/wiki/Q465274","display_name":"Ordinary differential equation","level":3,"score":0.643500804901123},{"id":"https://openalex.org/C34862557","wikidata":"https://www.wikidata.org/wiki/Q178985","display_name":"Ode","level":2,"score":0.5702925324440002},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5130175948143005},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.47372570633888245},{"id":"https://openalex.org/C77405623","wikidata":"https://www.wikidata.org/wiki/Q598451","display_name":"System dynamics","level":2,"score":0.46544063091278076},{"id":"https://openalex.org/C47822265","wikidata":"https://www.wikidata.org/wiki/Q854457","display_name":"Complex system","level":2,"score":0.4260403513908386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4225144386291504},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41179609298706055},{"id":"https://openalex.org/C78045399","wikidata":"https://www.wikidata.org/wiki/Q11214","display_name":"Differential equation","level":2,"score":0.345746785402298},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3447956442832947},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15890321135520935},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.09548351168632507},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3394486.3403132","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403132","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403132","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1908.06491","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1908.06491","pdf_url":"https://arxiv.org/pdf/1908.06491","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,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1908.06491","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1908.06491","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,"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":"article-journal"}],"best_oa_location":{"id":"doi:10.1145/3394486.3403132","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403132","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403132","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1153322124","display_name":null,"funder_award_id":"N00014-18-1-2585","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G3916121238","display_name":"CAREER: Interpretable Deep Modeling of Discrete Time Event Sequences","funder_award_id":"1750326","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6832078411","display_name":"III: Small: Collaborative Research: Comprehensive Heterogeneous Response Regression from Complex Data","funder_award_id":"1716432","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2949989211.pdf","grobid_xml":"https://content.openalex.org/works/W2949989211.grobid-xml"},"referenced_works_count":69,"referenced_works":["https://openalex.org/W583211095","https://openalex.org/W604736347","https://openalex.org/W1484977481","https://openalex.org/W1487127700","https://openalex.org/W1498436455","https://openalex.org/W1588906117","https://openalex.org/W1598796236","https://openalex.org/W1599969036","https://openalex.org/W1875112053","https://openalex.org/W1877916171","https://openalex.org/W1964333066","https://openalex.org/W1972309850","https://openalex.org/W2008620264","https://openalex.org/W2041684917","https://openalex.org/W2080829915","https://openalex.org/W2112090702","https://openalex.org/W2115682136","https://openalex.org/W2127048411","https://openalex.org/W2132105090","https://openalex.org/W2147824439","https://openalex.org/W2150533498","https://openalex.org/W2194775991","https://openalex.org/W2199258096","https://openalex.org/W2277170616","https://openalex.org/W2318146960","https://openalex.org/W2332939684","https://openalex.org/W2377801650","https://openalex.org/W2409622550","https://openalex.org/W2519887557","https://openalex.org/W2525748878","https://openalex.org/W2565330852","https://openalex.org/W2600297185","https://openalex.org/W2612538457","https://openalex.org/W2745013327","https://openalex.org/W2766453196","https://openalex.org/W2770527101","https://openalex.org/W2782210340","https://openalex.org/W2787740662","https://openalex.org/W2794363675","https://openalex.org/W2801182099","https://openalex.org/W2805516822","https://openalex.org/W2809425933","https://openalex.org/W2895352417","https://openalex.org/W2907855193","https://openalex.org/W2916106175","https://openalex.org/W2919115771","https://openalex.org/W2926003515","https://openalex.org/W2937628742","https://openalex.org/W2947473157","https://openalex.org/W2951629468","https://openalex.org/W2952191903","https://openalex.org/W2953253681","https://openalex.org/W2962975498","https://openalex.org/W2963112935","https://openalex.org/W2963302407","https://openalex.org/W2963312446","https://openalex.org/W2963755523","https://openalex.org/W2964051675","https://openalex.org/W2964121744","https://openalex.org/W2964145209","https://openalex.org/W2974916071","https://openalex.org/W3004739955","https://openalex.org/W3101913790","https://openalex.org/W3103322710","https://openalex.org/W3103720336","https://openalex.org/W3104097132","https://openalex.org/W3125447264","https://openalex.org/W4210257598","https://openalex.org/W4250883246"],"related_works":["https://openalex.org/W2951069524","https://openalex.org/W1440459627","https://openalex.org/W2963464736","https://openalex.org/W3004933931","https://openalex.org/W3129503389","https://openalex.org/W2995987465","https://openalex.org/W1993099985","https://openalex.org/W3001167728","https://openalex.org/W3204452360","https://openalex.org/W3201075350","https://openalex.org/W2782210340","https://openalex.org/W3016952441","https://openalex.org/W3169337152","https://openalex.org/W2122658551","https://openalex.org/W2809078955","https://openalex.org/W3087677820","https://openalex.org/W3119725804","https://openalex.org/W3044151669","https://openalex.org/W2138764356","https://openalex.org/W2220713948"],"abstract_inverted_index":{"Learning":[0],"continuous-time":[1,39,66,107,133],"dynamics":[2,67,108,135,181],"on":[3,68,109],"complex":[4,13,69,183],"networks":[5,70],"is":[6,22],"crucial":[7],"for":[8,132],"understanding,":[9],"predicting,":[10],"and":[11,17,42,59,143,180],"controlling":[12],"systems":[14,79,184],"in":[15,30,71,92,151,162,185],"science":[16],"engineering.":[18],"However,":[19],"this":[20],"task":[21],"very":[23],"challenging":[24],"due":[25],"to":[26,52,64,105],"the":[27,31,93,163,178],"combinatorial":[28],"complexities":[29],"structures":[32],"of":[33,83,89,175,182],"high":[34],"dimensional":[35],"systems,":[36],"their":[37,43],"elusive":[38],"nonlinear":[40],"dynamics,":[41],"structural-dynamic":[44],"dependencies.":[45],"To":[46],"address":[47],"these":[48],"challenges,":[49],"we":[50,96],"propose":[51],"combine":[53],"Ordinary":[54],"Differential":[55],"Equation":[56],"Systems":[57],"(ODEs)":[58],"Graph":[60,123],"Neural":[61,124],"Networks":[62],"(GNNs)":[63],"learn":[65],"a":[72,86,117,122,152,186],"data-driven":[73],"manner.":[74],"We":[75,155],"model":[76,112,120,128,158],"differential":[77],"equation":[78],"by":[80,159],"GNNs.":[81],"Instead":[82],"mapping":[84],"through":[85],"discrete":[87],"number":[88],"neural":[90],"layers":[91,99],"forward":[94],"process,":[95],"integrate":[97],"GNN":[98,119],"over":[100],"continuous":[101],"time":[102],"numerically,":[103],"leading":[104],"capturing":[106,177],"graphs.":[110],"Our":[111,127],"can":[113,129],"be":[114,130],"interpreted":[115],"as":[116],"Continuous-time":[118],"or":[121],"ODEs":[125],"model.":[126],"utilized":[131],"network":[134],"prediction,":[136],"structured":[137],"sequence":[138],"prediction":[139],"(a":[140,148],"regularly-sampled":[141],"case),":[142],"node":[144],"semi-supervised":[145],"classification":[146],"tasks":[147],"one-snapshot":[149],"case)":[150],"unified":[153,187],"framework.":[154,188],"validate":[156],"our":[157,172],"extensive":[160],"experiments":[161],"above":[164],"three":[165],"scenarios.":[166],"The":[167],"promising":[168],"experimental":[169],"results":[170],"demonstrate":[171],"model's":[173],"capability":[174],"jointly":[176],"structure":[179]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}