{"id":"https://openalex.org/W7168301404","doi":"https://doi.org/10.48550/arxiv.2607.10571","title":"Learning from Local Walks on Dynamic Graphs with Bandit Feedback","display_name":"Learning from Local Walks on Dynamic Graphs with Bandit Feedback","publication_year":2026,"publication_date":"2026-07-12","ids":{"openalex":"https://openalex.org/W7168301404","doi":"https://doi.org/10.48550/arxiv.2607.10571"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.10571","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10571","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.10571","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140619501","display_name":"Sourav Chakraborty","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chakraborty, Sourav","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140623012","display_name":"Amit Kiran Rege","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rege, Amit Kiran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140639928","display_name":"Claire Monteleoni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Monteleoni, Claire","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140663435","display_name":"Lijun Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Lijun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.958899974822998,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.958899974822998,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.011599999852478504,"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/T13553","display_name":"Age of Information Optimization","score":0.00860000029206276,"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/sublinear-function","display_name":"Sublinear function","score":0.7224000096321106},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.642799973487854},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5378000140190125},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4812999963760376},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.43560001254081726},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.367900013923645}],"concepts":[{"id":"https://openalex.org/C117160843","wikidata":"https://www.wikidata.org/wiki/Q338652","display_name":"Sublinear function","level":2,"score":0.7224000096321106},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.642799973487854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5605000257492065},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5378000140190125},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4812999963760376},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.42969998717308044},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39579999446868896},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3808000087738037},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.35089999437332153},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34290000796318054},{"id":"https://openalex.org/C199622910","wikidata":"https://www.wikidata.org/wiki/Q1128326","display_name":"Constraint satisfaction problem","level":3,"score":0.31859999895095825},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.28110000491142273},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C148764684","wikidata":"https://www.wikidata.org/wiki/Q621751","display_name":"Approximation algorithm","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2614000141620636},{"id":"https://openalex.org/C44616089","wikidata":"https://www.wikidata.org/wiki/Q30158686","display_name":"Constraint satisfaction","level":3,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.10571","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10571","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.10571","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10571","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,68],"study":[1],"stochastic":[2],"multi-armed":[3],"bandits":[4],"on":[5,75],"dynamic":[6],"graphs,":[7],"where":[8],"arms":[9],"correspond":[10],"to":[11,27,61],"the":[12,23,51,56,65,80],"vertices":[13],"of":[14,98],"a":[15,70,96,110,117,122],"network":[16],"with":[17],"time-varying":[18],"edges.":[19],"In":[20],"this":[21,92],"setting,":[22],"learner":[24,57],"is":[25,54],"restricted":[26],"local":[28,99],"movement,":[29],"selecting":[30],"only":[31],"its":[32],"current":[33],"node":[34],"or":[35],"an":[36],"immediate":[37],"neighbor":[38],"at":[39],"each":[40],"round.":[41],"This":[42],"constraint":[43],"decouples":[44],"best-arm":[45],"identification":[46],"from":[47],"exploitation:":[48],"even":[49],"after":[50],"optimal":[52],"arm":[53],"identified,":[55],"may":[58],"remain":[59],"unable":[60],"reach":[62],"it":[63],"through":[64],"evolving":[66],"topology.":[67],"identify":[69],"process-agnostic":[71],"structural":[72],"condition,":[73],"based":[74],"sliding-window":[76],"mixing,":[77],"that":[78],"ensures":[79],"graph's":[81],"intrinsic":[82],"walk":[83],"remains":[84],"stable":[85],"for":[86,113],"both":[87],"exploration":[88],"and":[89,102,121],"navigation.":[90],"Under":[91],"regime,":[93],"we":[94,115],"analyze":[95],"family":[97],"explore-then-commit":[100],"algorithms":[101],"establish":[103],"sublinear":[104],"expected":[105],"regret.":[106],"Our":[107],"framework":[108],"includes":[109],"reward-aware":[111],"strategy,":[112],"which":[114],"prove":[116],"worst-case":[118],"safety":[119],"theorem":[120],"separate":[123],"performance":[124],"gain":[125],"theorem.":[126]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-15T00:00:00"}
