{"id":"https://openalex.org/W4200324951","doi":"https://doi.org/10.1109/icnp52444.2021.9651937","title":"Generalizable and Interpretable Deep Learning for Network Congestion Prediction","display_name":"Generalizable and Interpretable Deep Learning for Network Congestion Prediction","publication_year":2021,"publication_date":"2021-11-01","ids":{"openalex":"https://openalex.org/W4200324951","doi":"https://doi.org/10.1109/icnp52444.2021.9651937"},"language":"en","primary_location":{"id":"doi:10.1109/icnp52444.2021.9651937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnp52444.2021.9651937","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","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/A5046120615","display_name":"Konstantinos Poularakis","orcid":"https://orcid.org/0000-0003-2570-3813"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Konstantinos Poularakis","raw_affiliation_strings":["Yale University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003936652","display_name":"Qiaofeng Qin","orcid":"https://orcid.org/0000-0002-8848-5916"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qiaofeng Qin","raw_affiliation_strings":["Yale University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057170324","display_name":"Franck Le","orcid":"https://orcid.org/0000-0002-0372-5045"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Franck Le","raw_affiliation_strings":["IBM Research, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007158362","display_name":"Sastry Kompella","orcid":"https://orcid.org/0000-0001-7696-6213"},"institutions":[{"id":"https://openalex.org/I1288214837","display_name":"United States Naval Research Laboratory","ror":"https://ror.org/04d23a975","country_code":"US","type":"facility","lineage":["https://openalex.org/I1288214837","https://openalex.org/I1330347796","https://openalex.org/I175003984","https://openalex.org/I3130687028"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sastry Kompella","raw_affiliation_strings":["Naval Research Laboratory, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Naval Research Laboratory, USA","institution_ids":["https://openalex.org/I1288214837"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014892027","display_name":"Leandros Tassiulas","orcid":"https://orcid.org/0000-0003-0932-774X"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leandros Tassiulas","raw_affiliation_strings":["Yale University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3188,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.84369194,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9973000288009644,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9438521862030029},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.8871120810508728},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7752859592437744},{"id":"https://openalex.org/keywords/network-topology","display_name":"Network topology","score":0.741777241230011},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6196749210357666},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5701965093612671},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.56987464427948},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5608848333358765},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5340477824211121},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5194896459579468},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4846985340118408},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3483322858810425},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.3217088580131531},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.14641091227531433},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08111882209777832}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9438521862030029},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.8871120810508728},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7752859592437744},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.741777241230011},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6196749210357666},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5701965093612671},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.56987464427948},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5608848333358765},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5340477824211121},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5194896459579468},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4846985340118408},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3483322858810425},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.3217088580131531},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.14641091227531433},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08111882209777832},{"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnp52444.2021.9651937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnp52444.2021.9651937","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320316514","display_name":"Arm","ror":"https://ror.org/04mmhzs81"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1970131865","https://openalex.org/W2090640027","https://openalex.org/W2111984406","https://openalex.org/W2116341502","https://openalex.org/W2158698691","https://openalex.org/W2170851227","https://openalex.org/W2613070411","https://openalex.org/W2744628735","https://openalex.org/W2768254111","https://openalex.org/W2775717462","https://openalex.org/W2781156794","https://openalex.org/W2883529420","https://openalex.org/W2907492528","https://openalex.org/W2919115771","https://openalex.org/W2925819646","https://openalex.org/W2953073956","https://openalex.org/W2953384591","https://openalex.org/W2963432161","https://openalex.org/W2968422580","https://openalex.org/W2968986602","https://openalex.org/W2977973695","https://openalex.org/W3009075394","https://openalex.org/W3038898937","https://openalex.org/W3089378901","https://openalex.org/W3091858314","https://openalex.org/W3098136764","https://openalex.org/W3106379731","https://openalex.org/W3125537303","https://openalex.org/W4287753834","https://openalex.org/W4288091680","https://openalex.org/W4297733535","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6673258281","https://openalex.org/W6713134421","https://openalex.org/W6739901393","https://openalex.org/W6745537798","https://openalex.org/W6754669440","https://openalex.org/W6773718218","https://openalex.org/W6840045202"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2008630378","https://openalex.org/W4361193272","https://openalex.org/W4310278675","https://openalex.org/W4378220270","https://openalex.org/W4319993887","https://openalex.org/W4297789176","https://openalex.org/W2768346313","https://openalex.org/W2963249138","https://openalex.org/W2998594699"],"abstract_inverted_index":{"While":[0],"recent":[1],"years":[2],"have":[3],"witnessed":[4],"a":[5,90,107],"steady":[6],"trend":[7],"of":[8,17,40,45,56,94,131,145,151],"applying":[9],"Deep":[10,20],"Learning":[11],"(DL)":[12],"to":[13,31,33,48,71,87,97,118],"networking":[14,58],"systems,":[15],"most":[16],"the":[18,43,46,54,57,99,125,143],"underlying":[19],"Neural":[21],"Networks":[22],"(DNNs)":[23],"suffer":[24],"two":[25],"major":[26],"limitations.":[27,101],"First,":[28],"they":[29],"fail":[30],"generalize":[32],"topologies":[34],"unseen":[35],"during":[36],"training.":[37],"This":[38],"lack":[39],"generalizability":[41],"hampers":[42],"ability":[44],"DNNs":[47,63,96],"make":[49,119],"good":[50],"decisions":[51],"every":[52],"time":[53,129],"topology":[55,127],"system":[59],"changes.":[60],"Second,":[61],"existing":[62],"commonly":[64],"operate":[65],"as":[66,134],"\"blackboxes\"":[67],"that":[68],"are":[69],"difficult":[70],"interpret":[72],"by":[73],"network":[74,108],"operators,":[75],"and":[76,112,128,155],"hinder":[77],"their":[78],"deployment":[79],"in":[80,149],"practice.":[81],"In":[82],"this":[83],"paper,":[84],"we":[85,104],"propose":[86],"rely":[88],"on":[89,106,137],"recently":[91],"developed":[92],"family":[93],"graph-based":[95],"address":[98],"aforementioned":[100],"More":[102],"specifically,":[103],"focus":[105],"congestion":[109,120],"prediction":[110,152],"application":[111],"apply":[113],"Graph":[114],"Attention":[115],"(GAT)":[116],"models":[117],"predictions":[121],"per":[122],"link":[123,132],"using":[124],"graph":[126],"series":[130],"loads":[133],"inputs.":[135],"Evaluations":[136],"three":[138],"real":[139],"backbone":[140],"networks":[141],"demonstrate":[142],"benefits":[144],"our":[146],"proposed":[147],"approach":[148],"terms":[150],"accuracy,":[153],"generalizability,":[154],"interpretability.":[156]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}