{"id":"https://openalex.org/W7123792281","doi":"https://doi.org/10.1109/iscc65549.2025.11326227","title":"Network Traffic Matrix Imputation via Large Language Models","display_name":"Network Traffic Matrix Imputation via Large Language Models","publication_year":2025,"publication_date":"2025-07-02","ids":{"openalex":"https://openalex.org/W7123792281","doi":"https://doi.org/10.1109/iscc65549.2025.11326227"},"language":null,"primary_location":{"id":"doi:10.1109/iscc65549.2025.11326227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscc65549.2025.11326227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Symposium on Computers and Communications (ISCC)","raw_type":"proceedings-article"},"type":"article","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/A5122914650","display_name":"Kaiwen Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kaiwen Jiang","raw_affiliation_strings":["Hefei University of Technology,School of Computer Science and Information Engineering,Hefei,China"],"affiliations":[{"raw_affiliation_string":"Hefei University of Technology,School of Computer Science and Information Engineering,Hefei,China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122931959","display_name":"Fenglin Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fenglin Yan","raw_affiliation_strings":["Hefei University of Technology,School of Computer Science and Information Engineering,Hefei,China"],"affiliations":[{"raw_affiliation_string":"Hefei University of Technology,School of Computer Science and Information Engineering,Hefei,China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100763035","display_name":"Yan Qiao","orcid":"https://orcid.org/0000-0002-4407-1762"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Qiao","raw_affiliation_strings":["Hefei University of Technology,School of Computer Science and Information Engineering,Hefei,China"],"affiliations":[{"raw_affiliation_string":"Hefei University of Technology,School of Computer Science and Information Engineering,Hefei,China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100457499","display_name":"Meng Li","orcid":"https://orcid.org/0000-0002-7101-8158"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Li","raw_affiliation_strings":["Hefei University of Technology,School of Computer Science and Information Engineering,Hefei,China"],"affiliations":[{"raw_affiliation_string":"Hefei University of Technology,School of Computer Science and Information Engineering,Hefei,China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122950522","display_name":"Yuxuan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yuxuan Li","raw_affiliation_strings":["The Hong Kong Polytechnic University,Faculty of Computer and Mathematical Science,Hong Kong,China"],"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University,Faculty of Computer and Mathematical Science,Hong Kong,China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122935427","display_name":"Mauro Conti","orcid":null},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Mauro Conti","raw_affiliation_strings":["University of Padua,Department of Mathematics and HIT Center,Padua,Italy"],"affiliations":[{"raw_affiliation_string":"University of Padua,Department of Mathematics and HIT Center,Padua,Italy","institution_ids":["https://openalex.org/I138689650"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5122914650"],"corresponding_institution_ids":["https://openalex.org/I16365422"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.68771195,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10714","display_name":"Software-Defined Networks and 5G","score":0.14910000562667847,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.14910000562667847,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.1168999969959259,"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/T11478","display_name":"Caching and Content Delivery","score":0.05829999968409538,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/imputation","display_name":"Imputation (statistics)","score":0.6177999973297119},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.516700029373169},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.45579999685287476},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43050000071525574},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4124999940395355},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.4101000130176544},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.4011000096797943},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.37630000710487366}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.769599974155426},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6177999973297119},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5390999913215637},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.516700029373169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5005000233650208},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47450000047683716},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.45579999685287476},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43050000071525574},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4124999940395355},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.37630000710487366},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.37310001254081726},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3643999993801117},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3589000105857849},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3402999937534332},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3330000042915344},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C167723999","wikidata":"https://www.wikidata.org/wiki/Q3773214","display_name":"Sampling distribution","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscc65549.2025.11326227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscc65549.2025.11326227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Symposium on Computers and Communications (ISCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311687","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1998675732","https://openalex.org/W2033154334","https://openalex.org/W2117368434","https://openalex.org/W2145563843","https://openalex.org/W2171676739","https://openalex.org/W2790209388","https://openalex.org/W2914231700","https://openalex.org/W2965446129","https://openalex.org/W2980225702","https://openalex.org/W2999389558","https://openalex.org/W3035382666","https://openalex.org/W3047551250","https://openalex.org/W3179935684","https://openalex.org/W4318953561","https://openalex.org/W4386243226","https://openalex.org/W4387876242","https://openalex.org/W4391470339","https://openalex.org/W4400409418","https://openalex.org/W4401508084","https://openalex.org/W4402753378","https://openalex.org/W4403721245","https://openalex.org/W4406207903"],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"have":[4],"demonstrated":[5],"remarkable":[6],"zero-shot":[7],"capabilities":[8],"across":[9],"various":[10],"domains.":[11],"This":[12],"paper":[13],"pioneers":[14],"the":[15,25,34,41,46,61,88,92,145,163,168,189,207],"application":[16,35],"of":[17,28,192],"LLMs\u2019":[18,121],"outstanding":[19],"knowledge":[20],"and":[21,45,206],"reasoning":[22],"abilities":[23],"to":[24,40,83,90,119],"challenging":[26],"task":[27],"Traffic":[29],"Matrix":[30],"(TM)":[31],"imputation.":[32,69],"However,":[33],"poses":[36],"significant":[37],"challenges":[38],"due":[39],"skewed":[42],"TM":[43,68,85,196],"distribution":[44],"deficient":[47],"traffic":[48,164],"feature":[49],"under":[50],"low":[51],"sampling":[52,169],"rate.":[53],"To":[54],"address":[55],"these":[56],"issues,":[57],"we":[58,77,103],"propose":[59],"TM-LLM,":[60],"first":[62],"LLM-based":[63],"model":[64],"specifically":[65],"designed":[66],"for":[67,176],"Our":[70],"approach":[71,118],"includes":[72],"two":[73],"critical":[74],"designs:":[75],"Firstly,":[76],"develop":[78],"an":[79],"adversarial":[80],"training":[81],"strategy":[82],"pre-impute":[84],"data,":[86],"allowing":[87],"LLM":[89],"understand":[91],"distributional":[93],"features":[94],"even":[95,166],"when":[96,167],"faced":[97],"with":[98,110,203],"extensive":[99],"missing":[100],"data.":[101],"Secondly,":[102],"devise":[104],"a":[105,111,136],"TM-specific":[106],"embedding":[107],"scheme":[108],"along":[109],"crafted":[112],"prompt":[113],"template,":[114],"which":[115],"enables":[116],"our":[117,178,204],"harness":[120],"exceptional":[122],"inferential":[123],"ability.":[124],"Experimental":[125],"results":[126],"show":[127],"that":[128],"TMLLM":[129],"significantly":[130],"outperforms":[131],"state-of-the-art":[132],"imputation":[133],"methods,":[134],"achieves":[135],"notable":[137],"16.5%":[138],"-44.8":[139],"%":[140,155],"improvement":[141],"in":[142,194],"accuracy":[143],"over":[144],"current":[146],"best":[147],"baseline,":[148],"while":[149],"reduces":[150],"measurement":[151],"costs":[152],"by":[153],"80":[154],"-":[156],"96":[157],"%.":[158],"It":[159],"can":[160],"accurately":[161],"capture":[162],"pattern":[165],"rate":[170],"is":[171,180],"extremely":[172],"low.":[173],"The":[174],"code":[175],"reproducing":[177],"experiments":[179],"publicly":[181],"available<sup":[182],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[183,199],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>.":[184],"These":[185],"findings":[186],"strongly":[187],"indicate":[188],"breakthrough":[190],"potential":[191],"LLMs":[193],"network":[195],"analysis":[197],"tasks.<sup":[198],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>The":[200],"experimental":[201],"codes":[202],"methods":[205],"datasets":[208],"are":[209],"available":[210],"at":[211],"https://github.com/FILingK/TM-LLM":[212]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-14T00:00:00"}