{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/exploring-partially-observed-networks-with","title":"Exploring Partially Observed Networks with Nonparametric Bandits","arxiv_id":"1804.07059","date":"2018-04-19","proceeding":null,"authors":["Kaushalya Madhawa","Tsuyoshi Murata"],"abstract":"Real-world networks such as social and communication networks are too large\nto be observed entirely. Such networks are often partially observed such that\nnetwork size, network topology, and nodes of the original network are unknown.\nIn this paper we formalize the Adaptive Graph Exploring problem. We assume that\nwe are given an incomplete snapshot of a large network and additional nodes can\nbe discovered by querying nodes in the currently observed network. The goal of\nthis problem is to maximize the number of observed nodes within a given query\nbudget. Querying which set of nodes maximizes the size of the observed network?\nWe formulate this problem as an exploration-exploitation problem and propose a\nnovel nonparametric multi-arm bandit (MAB) algorithm for identifying which\nnodes to be queried. Our contributions include: (1) $i$KNN-UCB, a novel\nnonparametric MAB algorithm, applies $k$-nearest neighbor UCB to the setting\nwhen the arms are presented in a vector space, (2) provide theoretical\nguarantee that $i$KNN-UCB algorithm has sublinear regret, and (3) applying\n$i$KNN-UCB algorithm on synthetic networks and real-world networks from\ndifferent domains, we show that our method discovers up to 40% more nodes\ncompared to existing baselines.","url_abs":"http://arxiv.org/abs/1804.07059v1","url_pdf":"http://arxiv.org/pdf/1804.07059v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"exploring-partially-observed-networks-with","repo_url":"https://bitbucket.org/kau_mad/net_complete","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}