{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108049"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108049","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Bayesian attributed network sampling","abstract":"We address the problem of sampling in attributed networks. While uniform sampling is a task independent sampling method, in real-world, this is often difficult to implement as it requires random access to all the nodes of graph. Link tracing sampling methods such as random Walk, expansion sampling overcome this problem, however they do not utilize the information provided by attributes of the nodes and just use the topology of the graph. We propose a network sampling method which is task independent and utilizes the node attributes. Our approach is based on introducing maximum unfamiliarity in each sampling step and it uses Bayesian approach to asses the familiarity of neighboring nodes with respect to the current sample.","abstract_html":"We address the problem of sampling in attributed networks. While uniform sampling is a task independent sampling method, in real-world, this is often difficult to implement as it requires random access to all the nodes of graph. Link tracing sampling methods such as random Walk, expansion sampling overcome this problem, however they do not utilize the information provided by attributes of the nodes and just use the topology of the graph. We propose a network sampling method which is task independent and utilizes the node attributes. Our approach is based on introducing maximum unfamiliarity in each sampling step and it uses Bayesian approach to asses the familiarity of neighboring nodes with respect to the current sample.","abstract_has_math":false,"creators":["Kumar, Ankit"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Sundaram, Hari"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T21:58:05Z","date_published":"2020-08-26T21:58:05Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Sampling","Data Mining","Social and Information Networks","Bayesian Analysis"],"languages":["en"],"rights":["Copyright 2020 Ankit Kumar"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108049","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sundaram, Hari"]},{"key":"dc:creator","label":"Author","values":["Kumar, Ankit"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T21:58:05Z","2020-05-13","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Sampling","Data Mining","Social and Information Networks","Bayesian Analysis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Ankit Kumar"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108049"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We address the problem of sampling in attributed networks. While uniform sampling is a task independent sampling method, in real-world, this is often difficult to implement as it requires random access to all the nodes of graph. Link tracing sampling methods such as random Walk, expansion sampling overcome this problem, however they do not utilize the information provided by attributes of the nodes and just use the topology of the graph. We propose a network sampling method which is task independent and utilizes the node attributes. Our approach is based on introducing maximum unfamiliarity in each sampling step and it uses Bayesian approach to asses the familiarity of neighboring nodes with respect to the current sample.","Submission original under an indefinite embargo labeled 'Open Access'. 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We propose a network sampling method which is task independent and utilizes the node attributes. Our approach is based on introducing maximum unfamiliarity in each sampling step and it uses Bayesian approach to asses the familiarity of neighboring nodes with respect to the current sample.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Ankit Kumar, accepted the attached license on 2020-05-12 at 15:00.","The student, Ankit Kumar, submitted this Thesis for approval on 2020-05-12 at 15:11.","This Thesis was approved for publication on 2020-05-13 at 16:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15362 on 2020-08-25 at 17:14:22","Made available in DSpace on 2020-08-26T21:58:05Z (GMT). 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