{"id":"https://openalex.org/W4405974750","doi":"https://doi.org/10.1109/pimrc59610.2024.10817182","title":"FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning","display_name":"FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning","publication_year":2024,"publication_date":"2024-09-02","ids":{"openalex":"https://openalex.org/W4405974750","doi":"https://doi.org/10.1109/pimrc59610.2024.10817182"},"language":"en","primary_location":{"id":"doi:10.1109/pimrc59610.2024.10817182","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc59610.2024.10817182","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-04573168/document","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017983865","display_name":"Duc-Thinh Ngo","orcid":"https://orcid.org/0000-0002-0842-7282"},"institutions":[{"id":"https://openalex.org/I19370010","display_name":"Orange (France)","ror":"https://ror.org/035j0tq82","country_code":"FR","type":"company","lineage":["https://openalex.org/I19370010"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Duc-Thinh Ngo","raw_affiliation_strings":["Cesson-S&#x00E9;vign&#x00E9;,Orange Innovation,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cesson-S&#x00E9;vign&#x00E9;,Orange Innovation,France","institution_ids":["https://openalex.org/I19370010"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049148352","display_name":"Kandaraj Piamrat","orcid":"https://orcid.org/0000-0002-4350-0254"},"institutions":[{"id":"https://openalex.org/I100445878","display_name":"\u00c9cole Centrale de Nantes","ror":"https://ror.org/03nh7d505","country_code":"FR","type":"education","lineage":["https://openalex.org/I100445878","https://openalex.org/I97188460"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]},{"id":"https://openalex.org/I4210117005","display_name":"Laboratoire des Sciences du Num\u00e9rique de Nantes","ror":"https://ror.org/02snf8m58","country_code":"FR","type":"facility","lineage":["https://openalex.org/I100445878","https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I205703379","https://openalex.org/I4210117005","https://openalex.org/I4210124215","https://openalex.org/I4210127572","https://openalex.org/I4210139971","https://openalex.org/I97188460","https://openalex.org/I97188460"]},{"id":"https://openalex.org/I4210127572","display_name":"IMT Atlantique","ror":"https://ror.org/030hj3061","country_code":"FR","type":"education","lineage":["https://openalex.org/I205703379","https://openalex.org/I4210127572"]},{"id":"https://openalex.org/I97188460","display_name":"Nantes Universit\u00e9","ror":"https://ror.org/03gnr7b55","country_code":"FR","type":"education","lineage":["https://openalex.org/I97188460"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Kandaraj Piamrat","raw_affiliation_strings":["Nantes University,&#x00C9;cole Centrale Nantes, IMT Atlantique, CNRS, INRIA, LS2N, UMR 6004,Nantes,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nantes University,&#x00C9;cole Centrale Nantes, IMT Atlantique, CNRS, INRIA, LS2N, UMR 6004,Nantes,France","institution_ids":["https://openalex.org/I4210127572","https://openalex.org/I97188460","https://openalex.org/I100445878","https://openalex.org/I1326498283","https://openalex.org/I4210117005","https://openalex.org/I1294671590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066189735","display_name":"Ons Aouedi","orcid":"https://orcid.org/0000-0002-2343-0850"},"institutions":[{"id":"https://openalex.org/I186903577","display_name":"University of Luxembourg","ror":"https://ror.org/036x5ad56","country_code":"LU","type":"education","lineage":["https://openalex.org/I186903577"]}],"countries":["LU"],"is_corresponding":false,"raw_author_name":"Ons Aouedi","raw_affiliation_strings":["University of Luxembourg,SnT, SIGCOM,Luxembourg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Luxembourg,SnT, SIGCOM,Luxembourg","institution_ids":["https://openalex.org/I186903577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074474915","display_name":"Thomas Hassan","orcid":"https://orcid.org/0000-0003-2912-1984"},"institutions":[{"id":"https://openalex.org/I19370010","display_name":"Orange (France)","ror":"https://ror.org/035j0tq82","country_code":"FR","type":"company","lineage":["https://openalex.org/I19370010"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Thomas Hassan","raw_affiliation_strings":["Cesson-S&#x00E9;vign&#x00E9;,Orange Innovation,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cesson-S&#x00E9;vign&#x00E9;,Orange Innovation,France","institution_ids":["https://openalex.org/I19370010"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032951800","display_name":"Philippe Raipin","orcid":null},"institutions":[{"id":"https://openalex.org/I19370010","display_name":"Orange (France)","ror":"https://ror.org/035j0tq82","country_code":"FR","type":"company","lineage":["https://openalex.org/I19370010"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Philippe Raipin","raw_affiliation_strings":["Cesson-S&#x00E9;vign&#x00E9;,Orange Innovation,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cesson-S&#x00E9;vign&#x00E9;,Orange Innovation,France","institution_ids":["https://openalex.org/I19370010"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2165,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55838723,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7942296266555786},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5454069972038269},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.527737021446228},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4997673034667969},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49470803141593933},{"id":"https://openalex.org/keywords/cellular-network","display_name":"Cellular network","score":0.4647741913795471},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4563707411289215},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44686242938041687},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.44278496503829956},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.19809463620185852},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.14331412315368652}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7942296266555786},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5454069972038269},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.527737021446228},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4997673034667969},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49470803141593933},{"id":"https://openalex.org/C153646914","wikidata":"https://www.wikidata.org/wiki/Q535695","display_name":"Cellular network","level":2,"score":0.4647741913795471},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4563707411289215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44686242938041687},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.44278496503829956},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.19809463620185852},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.14331412315368652},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/pimrc59610.2024.10817182","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc59610.2024.10817182","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-04573168v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04573168v1/file/conference_101719.pdf","pdf_url":"https://hal.science/hal-04573168/document","source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Sep 2024, Valencia (Espagne), Spain. &#x27E8;10.1109/PIMRC59610.2024.10817182&#x27E9;","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:orbilu.uni.lu:10993/61428","is_oa":true,"landing_page_url":"https://orbilu.uni.lu/handle/10993/61428","pdf_url":"https://orbilu.uni.lu/bitstream/10993/61428/1/2405.08843v1.pdf","source":{"id":"https://openalex.org/S4306401815","display_name":"Open Repository and Bibliography (University of Luxembourg)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I186903577","host_organization_name":"University of Luxembourg","host_organization_lineage":["https://openalex.org/I186903577"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive  Graph-Based Learning (2024); IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2024), Valencia, Spain [ES], 2\u20135 September 2024","raw_type":"http://purl.org/coar/resource_type/c_5794"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-04573168v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04573168v1/file/conference_101719.pdf","pdf_url":"https://hal.science/hal-04573168/document","source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Sep 2024, Valencia (Espagne), Spain. &#x27E8;10.1109/PIMRC59610.2024.10817182&#x27E9;","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"display_name":"Gender equality","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/5"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4405974750.pdf"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W569478347","https://openalex.org/W2064675550","https://openalex.org/W2097117768","https://openalex.org/W2106411961","https://openalex.org/W2519091744","https://openalex.org/W2949676527","https://openalex.org/W2964321699","https://openalex.org/W3038981236","https://openalex.org/W3080253043","https://openalex.org/W3158058297","https://openalex.org/W3208403094","https://openalex.org/W4205209477","https://openalex.org/W4206755733","https://openalex.org/W4221146484","https://openalex.org/W4285138331","https://openalex.org/W4376312534","https://openalex.org/W4385568303","https://openalex.org/W6638667902","https://openalex.org/W6676179485","https://openalex.org/W6720006811","https://openalex.org/W6736685754","https://openalex.org/W6746015598","https://openalex.org/W6754929296","https://openalex.org/W6780221082"],"related_works":["https://openalex.org/W2770234245","https://openalex.org/W96612179","https://openalex.org/W4229499248","https://openalex.org/W2566006169","https://openalex.org/W1567818861","https://openalex.org/W2987774938","https://openalex.org/W4256492088","https://openalex.org/W632915154","https://openalex.org/W2055733372","https://openalex.org/W3022067003"],"abstract_inverted_index":{"From":[0],"a":[1,48,85,91],"telecommunication":[2],"standpoint,":[3],"the":[4,65,74,141],"surge":[5],"in":[6,144],"users":[7],"and":[8,17,32,68,90],"services":[9],"challenges":[10],"next-generation":[11],"networks":[12],"with":[13,104,120,147],"escalating":[14],"traffic":[15,21,53,61,103],"demands":[16],"limited":[18],"resources.":[19],"Accurate":[20],"prediction":[22],"can":[23,97,113],"offer":[24],"network":[25,30],"operators":[26],"valuable":[27],"insights":[28],"into":[29],"conditions":[31],"suggest":[33],"optimal":[34],"allocation":[35],"policies.":[36],"Recently,":[37],"spatio-temporal":[38],"forecasting,":[39],"employing":[40],"Graph":[41],"Neural":[42],"Networks":[43],"(GNNs),":[44],"has":[45],"emerged":[46],"as":[47],"promising":[49],"method":[50],"for":[51],"cellular":[52,102],"prediction.":[54],"However,":[55],"existing":[56],"studies,":[57],"inspired":[58],"by":[59,117],"road":[60],"forecasting":[62,94],"formulations,":[63],"overlook":[64],"dynamic":[66],"deployment":[67],"removal":[69],"of":[70,101],"base":[71],"stations,":[72],"requiring":[73],"GNN-based":[75,93],"forecaster":[76],"to":[77,126,133,140,151],"handle":[78],"an":[79],"evolving":[80],"graph.":[81],"This":[82],"work":[83],"introduces":[84],"novel":[86],"inductive":[87],"learning":[88,119],"scheme":[89],"generalizable":[92],"model":[95,112],"that":[96,110],"process":[98],"diverse":[99],"graphs":[100],"one-time":[105],"training.":[106],"We":[107],"also":[108],"demonstrate":[109],"this":[111],"be":[114],"easily":[115],"leveraged":[116],"transfer":[118],"minimal":[121],"effort,":[122],"making":[123],"it":[124],"applicable":[125],"different":[127],"areas.":[128],"Experimental":[129],"results":[130],"show":[131],"up":[132],"${9.":[134],"8":[135],"\\%}$":[136],"performance":[137],"improvement":[138],"compared":[139],"state-of-the-art,":[142],"especially":[143],"rare-data":[145],"settings":[146],"training":[148],"data":[149],"reduced":[150],"below":[152],"$20":[153],"\\%$.":[154]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}
