{"id":"https://openalex.org/W2585055079","doi":"https://doi.org/10.1109/bigdata.2016.7841070","title":"Max-node sampling: An expansion-densification algorithm for data collection","display_name":"Max-node sampling: An expansion-densification algorithm for data collection","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2585055079","doi":"https://doi.org/10.1109/bigdata.2016.7841070","mag":"2585055079"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2016.7841070","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7841070","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Big Data (Big Data)","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/A5050296572","display_name":"Katchaguy Areekijseree","orcid":null},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Katchaguy Areekijseree","raw_affiliation_strings":["Department of EECS, Syracuse University, Syracuse, NY, U.S.A"],"affiliations":[{"raw_affiliation_string":"Department of EECS, Syracuse University, Syracuse, NY, U.S.A","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074046047","display_name":"Ricky Laishram","orcid":null},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ricky Laishram","raw_affiliation_strings":["Department of EECS, Syracuse University, Syracuse, NY, U.S.A"],"affiliations":[{"raw_affiliation_string":"Department of EECS, Syracuse University, Syracuse, NY, U.S.A","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062221607","display_name":"Sucheta Soundarajan","orcid":"https://orcid.org/0000-0003-1166-4067"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sucheta Soundarajan","raw_affiliation_strings":["Department of EECS, Syracuse University, Syracuse, NY, U.S.A"],"affiliations":[{"raw_affiliation_string":"Department of EECS, Syracuse University, Syracuse, NY, U.S.A","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5050296572"],"corresponding_institution_ids":["https://openalex.org/I70983195"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.15889465,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"2016","issue":null,"first_page":"3944","last_page":"3946"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.9983999729156494,"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"}},{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/intuition","display_name":"Intuition","score":0.7595127820968628},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6565272808074951},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6345850825309753},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6081432700157166},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5578780174255371},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5080926418304443},{"id":"https://openalex.org/keywords/budget-constraint","display_name":"Budget constraint","score":0.4545922875404358},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4429433345794678},{"id":"https://openalex.org/keywords/phase-transition","display_name":"Phase transition","score":0.4219595491886139},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32236015796661377},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23070812225341797},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09067106246948242},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08164864778518677}],"concepts":[{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.7595127820968628},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6565272808074951},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6345850825309753},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6081432700157166},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5578780174255371},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5080926418304443},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.4545922875404358},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4429433345794678},{"id":"https://openalex.org/C149288129","wikidata":"https://www.wikidata.org/wiki/Q185357","display_name":"Phase transition","level":2,"score":0.4219595491886139},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32236015796661377},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23070812225341797},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09067106246948242},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08164864778518677},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C133425853","wikidata":"https://www.wikidata.org/wiki/Q60571","display_name":"Neoclassical economics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata.2016.7841070","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7841070","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"mag:2786610312","is_oa":false,"landing_page_url":"http://jglobal.jst.go.jp/en/public/20090422/201702257423528329","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2026840599","https://openalex.org/W2136486572","https://openalex.org/W2146008005","https://openalex.org/W2171935404","https://openalex.org/W4230973533"],"related_works":["https://openalex.org/W83893804","https://openalex.org/W2178649091","https://openalex.org/W2353238953","https://openalex.org/W30906829","https://openalex.org/W2496037054","https://openalex.org/W2024895661","https://openalex.org/W2390883384","https://openalex.org/W1862027024","https://openalex.org/W2054108430","https://openalex.org/W3123594720"],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3],"propose":[4],"Max-Node":[5,17,33],"sampling,":[6],"a":[7,30],"novel":[8],"sampling":[9],"algorithm":[10,77],"for":[11],"data":[12],"collection.":[13],"The":[14,71,82],"goal":[15,83],"of":[16,23,61,75,84,119],"is":[18,34,65,88],"to":[19,54,57,67,89,91,99,121],"maximize":[20,58],"the":[21,27,37,59,85,95,105,124],"number":[22,60],"nodes":[24,62,103],"observed":[25],"in":[26,104],"sample,":[28],"given":[29],"budget":[31],"constraint.":[32],"based":[35],"on":[36,111],"intuition":[38],"that":[39,48],"networks":[40],"contain":[41],"many":[42,102],"densely":[43],"connected":[44,53],"regions":[45],"(i.e.,":[46],"communities),":[47],"may":[49],"be":[50],"only":[51],"weakly":[52],"another,":[55],"and":[56,80,115],"observed,":[63],"it":[64],"critical":[66],"transition":[68,90],"between":[69],"communities.":[70],"two":[72],"key":[73],"phases":[74],"our":[76],"are":[78],"Expansion":[79,86],"Densification.":[81],"phase":[87,97],"unobserved":[92],"regions,":[93],"while":[94],"Densification":[96],"aims":[98],"collect":[100],"as":[101],"current":[106],"community.":[107],"We":[108],"conduct":[109],"experiments":[110],"several":[112],"real":[113],"networks,":[114],"show":[116],"an":[117],"improvement":[118],"up":[120],"40%":[122],"vs.":[123],"baselines.":[125]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
