{"id":"https://openalex.org/W4415368101","doi":"https://doi.org/10.1109/isit63088.2025.11195482","title":"On Statistical Estimation of Edge-Reinforced Random Walks","display_name":"On Statistical Estimation of Edge-Reinforced Random Walks","publication_year":2025,"publication_date":"2025-06-22","ids":{"openalex":"https://openalex.org/W4415368101","doi":"https://doi.org/10.1109/isit63088.2025.11195482"},"language":null,"primary_location":{"id":"doi:10.1109/isit63088.2025.11195482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit63088.2025.11195482","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Information Theory (ISIT)","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/A5101923984","display_name":"Qi Ding","orcid":"https://orcid.org/0000-0002-2269-7051"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Qinghua Devon Ding","raw_affiliation_strings":["University of California at Berkeley,Department of Electrical Engineering and Computer Sciences,Berkeley,CA,United States"],"affiliations":[{"raw_affiliation_string":"University of California at Berkeley,Department of Electrical Engineering and Computer Sciences,Berkeley,CA,United States","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013689450","display_name":"Venkat Anantharam","orcid":"https://orcid.org/0000-0002-6214-7927"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Venkat Anantharam","raw_affiliation_strings":["University of California at Berkeley,Department of Electrical Engineering and Computer Sciences,Berkeley,CA,United States"],"affiliations":[{"raw_affiliation_string":"University of California at Berkeley,Department of Electrical Engineering and Computer Sciences,Berkeley,CA,United States","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101923984"],"corresponding_institution_ids":["https://openalex.org/I95457486"],"apc_list":null,"apc_paid":null,"fwci":2.3568,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91488772,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9459999799728394,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9459999799728394,"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/random-walk","display_name":"Random walk","score":0.7387999892234802},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6225000023841858},{"id":"https://openalex.org/keywords/random-variate","display_name":"Random variate","score":0.5424000024795532},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.43799999356269836},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.41940000653266907},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4072999954223633},{"id":"https://openalex.org/keywords/random-function","display_name":"Random function","score":0.39259999990463257},{"id":"https://openalex.org/keywords/random-field","display_name":"Random field","score":0.3560999929904938},{"id":"https://openalex.org/keywords/heterogeneous-random-walk-in-one-dimension","display_name":"Heterogeneous random walk in one dimension","score":0.33399999141693115}],"concepts":[{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.7387999892234802},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6225000023841858},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5504000186920166},{"id":"https://openalex.org/C141547133","wikidata":"https://www.wikidata.org/wiki/Q7291996","display_name":"Random variate","level":3,"score":0.5424000024795532},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.43799999356269836},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.41940000653266907},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4088999927043915},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4072999954223633},{"id":"https://openalex.org/C13929819","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Random function","level":3,"score":0.39259999990463257},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.38100001215934753},{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3479999899864197},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3382999897003174},{"id":"https://openalex.org/C154072304","wikidata":"https://www.wikidata.org/wiki/Q5747218","display_name":"Heterogeneous random walk in one dimension","level":3,"score":0.33399999141693115},{"id":"https://openalex.org/C20353970","wikidata":"https://www.wikidata.org/wiki/Q1056998","display_name":"Simple random sample","level":3,"score":0.326200008392334},{"id":"https://openalex.org/C200985842","wikidata":"https://www.wikidata.org/wiki/Q3375503","display_name":"Random permutation","level":3,"score":0.3239000141620636},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C2779172887","wikidata":"https://www.wikidata.org/wiki/Q184316","display_name":"PageRank","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C52386014","wikidata":"https://www.wikidata.org/wiki/Q166211","display_name":"Random element","level":3,"score":0.2924000024795532},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.29120001196861267},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.2639999985694885},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C138405894","wikidata":"https://www.wikidata.org/wiki/Q3179949","display_name":"Multivariate random variable","level":3,"score":0.25940001010894775},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25780001282691956},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.2540999948978424},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit63088.2025.11195482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit63088.2025.11195482","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G705888033","display_name":null,"funder_award_id":"CCF-1901004,CIF-2007965","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1636931539","https://openalex.org/W2064368638","https://openalex.org/W2066636486","https://openalex.org/W2087737878","https://openalex.org/W2091944486","https://openalex.org/W2117535553","https://openalex.org/W2147406357","https://openalex.org/W2153610560","https://openalex.org/W2471517265","https://openalex.org/W2962764277","https://openalex.org/W3005854412","https://openalex.org/W3037346199","https://openalex.org/W3107422154","https://openalex.org/W4361303334","https://openalex.org/W4415368101"],"related_works":[],"abstract_inverted_index":{"Reinforced":[0],"random":[1,6,12,84,88,122,130],"walks":[2,7,13,85],"(RRWs),":[3],"including":[4],"vertex-reinforced":[5],"(VRRWs)":[8],"and":[9,46,83,104,132,148],"edge-reinforced":[10],"reinforced":[11,43],"(ERRWs),":[14],"model":[15],"phenomena":[16],"where":[17],"transition":[18],"probabilities":[19],"evolve":[20],"based":[21,97],"on":[22,66,98],"prior":[23],"visitation":[24],"history":[25],"[5],":[26],"[8],":[27],"[15],":[28],"[16].":[29],"These":[30,138],"models":[31],"have":[32],"found":[33],"applications":[34,150],"in":[35,86,120],"various":[36],"areas,":[37],"such":[38],"as":[39],"network":[40],"embedding":[41],"[18],":[42],"PageRank":[44],"[6],":[45],"modeling":[47],"animal":[48],"behaviors":[49],"[14],":[50],"among":[51],"others.":[52],"However,":[53],"statistical":[54,147],"estimation":[55],"of":[56,72,102,128,145,151],"the":[57,68,79,99,105,110,115,121,126,129,142],"parameters":[58],"governing":[59],"RRWs":[60],"remains":[61],"underexplored.":[62],"This":[63],"work":[64],"focuses":[65],"estimating":[67],"initial":[69],"edge":[70],"weights":[71],"ERRWs":[73],"using":[74],"observed":[75],"trajectory":[76],"data.":[77],"Leveraging":[78],"connections":[80],"between":[81],"ERRW":[82],"a":[87],"environment":[89,123],"(RWRE)":[90],"[9],":[91],"[10],":[92],"we":[93,113],"propose":[94],"an":[95],"estimator":[96],"generalized":[100],"method":[101],"moments":[103],"\u201cmagic":[106],"formula\u201d.":[107],"To":[108],"analyze":[109],"sample":[111,135],"complexity,":[112],"exploit":[114],"hyperbolic":[116],"Gaussian":[117],"structure":[118],"embedded":[119],"to":[124,141],"bound":[125],"order":[127],"conductance,":[131],"hence":[133],"derive":[134],"complexity":[136],"bounds.":[137],"findings":[139],"contribute":[140],"theoretical":[143],"foundation":[144],"promising":[146],"algorithmic":[149],"ERRWs.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-21T00:00:00"}
