{"id":"https://openalex.org/W4407639097","doi":"https://doi.org/10.1109/ton.2025.3540091","title":"Spatio-Temporal Anomaly Detection for 5G-Clusters: A Multi-Scale Fuzzy Contrastive Learning Approach","display_name":"Spatio-Temporal Anomaly Detection for 5G-Clusters: A Multi-Scale Fuzzy Contrastive Learning Approach","publication_year":2025,"publication_date":"2025-02-17","ids":{"openalex":"https://openalex.org/W4407639097","doi":"https://doi.org/10.1109/ton.2025.3540091"},"language":"en","primary_location":{"id":"doi:10.1109/ton.2025.3540091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ton.2025.3540091","pdf_url":null,"source":{"id":"https://openalex.org/S5407042750","display_name":"IEEE Transactions on Networking","issn_l":"2998-4157","issn":["2998-4157"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Networking","raw_type":"journal-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/A5051857096","display_name":"Chao Luo","orcid":"https://orcid.org/0000-0002-0925-5988"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luo Chao","raw_affiliation_strings":["School of Information Science and Engineering, Shandong Normal University, Jinan, China"],"raw_orcid":"https://orcid.org/0000-0002-0925-5988","affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100413684","display_name":"Yinghua Li","orcid":"https://orcid.org/0000-0003-1390-0393"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Yinghua","raw_affiliation_strings":["School of Information Science and Engineering, Shandong Normal University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067105491","display_name":"Fengqian Ding","orcid":"https://orcid.org/0000-0001-8238-0243"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ding Fengqian","raw_affiliation_strings":["School of Information Science and Engineering, Shandong University, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Shandong University, Qingdao, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"last","author":{"id":null,"display_name":"Shao Rui","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shao Rui","raw_affiliation_strings":["Center of Network Optimization, Mobile Shandong Company Ltd, Jinan, China","Center of Network Optimization, China Mobile Shandong Company Ltd, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center of Network Optimization, Mobile Shandong Company Ltd, Jinan, China","institution_ids":["https://openalex.org/I180662265"]},{"raw_affiliation_string":"Center of Network Optimization, China Mobile Shandong Company Ltd, Jinan, China","institution_ids":["https://openalex.org/I180662265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.217,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.94654847,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"33","issue":"4","first_page":"1588","last_page":"1602"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9810000061988831,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9810000061988831,"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/T12391","display_name":"Artificial Immune Systems Applications","score":0.9376000165939331,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.6616442799568176},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5846651196479797},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.56224125623703},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5553035736083984},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5024173259735107},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4490634799003601},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3477928042411804},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.19160789251327515},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.1444385051727295},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07596030831336975}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6616442799568176},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5846651196479797},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.56224125623703},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5553035736083984},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5024173259735107},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4490634799003601},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3477928042411804},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.19160789251327515},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.1444385051727295},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07596030831336975},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ton.2025.3540091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ton.2025.3540091","pdf_url":null,"source":{"id":"https://openalex.org/S5407042750","display_name":"IEEE Transactions on Networking","issn_l":"2998-4157","issn":["2998-4157"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Networking","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1190329884","display_name":null,"funder_award_id":"62172264","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3663634948","display_name":null,"funder_award_id":"ZR2019MF020","funder_id":"https://openalex.org/F4320336925","funder_display_name":"Shandong Provincial Postdoctoral Science Foundation"},{"id":"https://openalex.org/G6600174315","display_name":null,"funder_award_id":"ZR2019MF020","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null},{"id":"https://openalex.org/F4320336925","display_name":"Shandong Provincial Postdoctoral Science Foundation","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1530232915","https://openalex.org/W1934371139","https://openalex.org/W2056081083","https://openalex.org/W2058063871","https://openalex.org/W2167967165","https://openalex.org/W2296719434","https://openalex.org/W2743617586","https://openalex.org/W2785362611","https://openalex.org/W2911200746","https://openalex.org/W2912126994","https://openalex.org/W2950361482","https://openalex.org/W2953352227","https://openalex.org/W2961319904","https://openalex.org/W2962736999","https://openalex.org/W2963166639","https://openalex.org/W3001110895","https://openalex.org/W3018019331","https://openalex.org/W3023371261","https://openalex.org/W3035725276","https://openalex.org/W3042440267","https://openalex.org/W3093410479","https://openalex.org/W3169450514","https://openalex.org/W3170981104","https://openalex.org/W3175177406","https://openalex.org/W3186740887","https://openalex.org/W3190152617","https://openalex.org/W3191802839","https://openalex.org/W3192648431","https://openalex.org/W3204016334","https://openalex.org/W3206326321","https://openalex.org/W3210350882","https://openalex.org/W4206118545","https://openalex.org/W4226298073","https://openalex.org/W4230765542","https://openalex.org/W4241492760","https://openalex.org/W4254182148","https://openalex.org/W4281955767","https://openalex.org/W4284711968","https://openalex.org/W4285505260","https://openalex.org/W4310014952","https://openalex.org/W4319300106","https://openalex.org/W4327568850","https://openalex.org/W4353033316","https://openalex.org/W4360995211","https://openalex.org/W4379983699","https://openalex.org/W4385245566","https://openalex.org/W4385562572","https://openalex.org/W4385731906","https://openalex.org/W6748102297","https://openalex.org/W6759891849","https://openalex.org/W6781695082","https://openalex.org/W6785773631","https://openalex.org/W6788705105","https://openalex.org/W6795961747","https://openalex.org/W7016021835"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4377864969","https://openalex.org/W3120251014"],"abstract_inverted_index":{"Anomaly":[0,54],"detection":[1,49,81,123,146],"is":[2,36,85,168],"an":[3,38],"important":[4],"data-mining":[5],"task":[6],"closely":[7],"related":[8],"to":[9,30,69,159],"specific":[10],"applications.":[11],"In":[12,41],"recent":[13],"years,":[14],"spatio-temporal":[15,65],"data":[16,35,62],"deriving":[17],"from":[18,24,63,75],"various":[19],"networks":[20],"has":[21,140],"been":[22,141],"collected":[23],"many":[25],"real-world":[26],"scenarios,":[27],"but":[28],"how":[29],"detect":[31],"anomalies":[32],"in":[33,124,153,157],"such":[34],"still":[37],"open":[39],"problem.":[40],"this":[42,83],"article,":[43],"we":[44],"propose":[45],"a":[46,111],"novel":[47],"anomaly":[48,80,122,145],"framework,":[50],"Multi-scale":[51],"Fuzzy":[52],"Contrastive":[53],"Detection":[55],"(MFCAD),":[56],"by":[57,128],"capturing":[58],"anomalous":[59],"patterns":[60],"of":[61,91,95,102,106,147,163],"multiple":[64],"scales":[66],"so":[67],"as":[68],"learn":[70],"distinguishable":[71],"feature":[72],"representations.":[73],"Different":[74],"conventional":[76],"reconstruction-":[77],"or":[78],"prediction-based":[79],"methods,":[82],"approach":[84],"not":[86],"concerned":[87],"with":[88],"the":[89,92,96,100,103,107,125,130,144,161,164,178],"consistency":[90],"encoded":[93],"representation":[94,114],"implicit":[97,104,135],"layer":[98,105],"and":[99,119,134,174],"discriminability":[101],"anomalies.":[108],"MFCAD":[109],"implements":[110],"more":[112],"discriminative":[113],"using":[115],"fuzzy":[116],"contrastive":[117],"learning":[118],"explicitly":[120],"performs":[121],"potential":[126],"space":[127],"measuring":[129],"distance":[131],"between":[132],"outliers":[133],"values.":[136],"This":[137],"proposed":[138,165],"method":[139],"applied":[142],"for":[143],"5G":[148],"mobile":[149],"network":[150],"clusters":[151],"(5G-MNCs)":[152],"China":[154],"Mobile.":[155],"Furthermore,":[156],"order":[158],"validate":[160],"generalizability":[162],"method,":[166],"it":[167],"further":[169],"tested":[170],"on":[171],"public":[172],"datasets":[173],"experiment":[175],"results":[176],"show":[177],"promising":[179],"performance.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}