{"id":"https://openalex.org/W2755881136","doi":"https://doi.org/10.1109/icccn.2017.8038387","title":"ForestStream: Accurate Measurement of Cascades in Online Social Networks","display_name":"ForestStream: Accurate Measurement of Cascades in Online Social Networks","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2755881136","doi":"https://doi.org/10.1109/icccn.2017.8038387","mag":"2755881136"},"language":"en","primary_location":{"id":"doi:10.1109/icccn.2017.8038387","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccn.2017.8038387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 26th International Conference on Computer Communication and Networks (ICCCN)","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/A5110720906","display_name":"Long Gong","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Long Gong","raw_affiliation_strings":["Georgia Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032154791","display_name":"Lanxi Huang","orcid":"https://orcid.org/0000-0002-9374-3772"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lanxi Huang","raw_affiliation_strings":["Georgia Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023909481","display_name":"Paul Tune","orcid":"https://orcid.org/0000-0002-2459-3168"},"institutions":[{"id":"https://openalex.org/I5681781","display_name":"University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I5681781"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Paul Tune","raw_affiliation_strings":["University of Adelaide"],"affiliations":[{"raw_affiliation_string":"University of Adelaide","institution_ids":["https://openalex.org/I5681781"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012872041","display_name":"Jinyoung Han","orcid":"https://orcid.org/0000-0002-8911-2791"},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jinyoung Han","raw_affiliation_strings":["Hanyang University"],"affiliations":[{"raw_affiliation_string":"Hanyang University","institution_ids":["https://openalex.org/I4575257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017383502","display_name":"Chen\u2010Nee Chuah","orcid":"https://orcid.org/0000-0002-2772-387X"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen-Nee Chuah","raw_affiliation_strings":["University of California Davis"],"affiliations":[{"raw_affiliation_string":"University of California Davis","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016001264","display_name":"Matthew Roughan","orcid":"https://orcid.org/0000-0002-7882-7329"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matthew Roughan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5025728584","display_name":"Jun Xu","orcid":"https://orcid.org/0000-0002-0046-8119"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Xu","raw_affiliation_strings":["Georgia Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5110720906"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":0.172,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.49048546,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T10742","display_name":"Peer-to-Peer Network Technologies","score":0.998199999332428,"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/T10799","display_name":"Data Visualization and Analytics","score":0.9829999804496765,"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/computer-science","display_name":"Computer science","score":0.8318238258361816},{"id":"https://openalex.org/keywords/cascade","display_name":"Cascade","score":0.6958475112915039},{"id":"https://openalex.org/keywords/dissemination","display_name":"Dissemination","score":0.6134511828422546},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5607123970985413},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.517447292804718},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5171324610710144},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.47162628173828125},{"id":"https://openalex.org/keywords/information-cascade","display_name":"Information cascade","score":0.44738131761550903},{"id":"https://openalex.org/keywords/simple-random-sample","display_name":"Simple random sample","score":0.4303818345069885},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.419047474861145},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35160279273986816},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3384735584259033},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07760012149810791}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8318238258361816},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.6958475112915039},{"id":"https://openalex.org/C101780184","wikidata":"https://www.wikidata.org/wiki/Q840576","display_name":"Dissemination","level":2,"score":0.6134511828422546},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5607123970985413},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.517447292804718},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5171324610710144},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.47162628173828125},{"id":"https://openalex.org/C27286358","wikidata":"https://www.wikidata.org/wiki/Q6031027","display_name":"Information cascade","level":2,"score":0.44738131761550903},{"id":"https://openalex.org/C20353970","wikidata":"https://www.wikidata.org/wiki/Q1056998","display_name":"Simple random sample","level":3,"score":0.4303818345069885},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.419047474861145},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35160279273986816},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3384735584259033},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07760012149810791},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccn.2017.8038387","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccn.2017.8038387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 26th International Conference on Computer Communication and Networks (ICCCN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1514107797","https://openalex.org/W2016289973","https://openalex.org/W2086976405","https://openalex.org/W2103799649","https://openalex.org/W2105277971","https://openalex.org/W2106315062","https://openalex.org/W2108614537","https://openalex.org/W2111708605","https://openalex.org/W2117853077","https://openalex.org/W2119885577","https://openalex.org/W2123845384","https://openalex.org/W2124430127","https://openalex.org/W2133777675","https://openalex.org/W2144501280","https://openalex.org/W2146008005","https://openalex.org/W2498094064","https://openalex.org/W6630727257","https://openalex.org/W6672459623"],"related_works":["https://openalex.org/W2038891404","https://openalex.org/W3104987177","https://openalex.org/W2810784123","https://openalex.org/W3041929574","https://openalex.org/W2990751567","https://openalex.org/W4200012112","https://openalex.org/W2219275368","https://openalex.org/W4385988053","https://openalex.org/W1967715387","https://openalex.org/W4292369391"],"abstract_inverted_index":{"Various":[0],"Online":[1],"Social":[2],"Network":[3],"(OSN)":[4],"based":[5],"applications":[6,31],"depend":[7],"on":[8,94],"the":[9,84,102,106,124],"interactions":[10],"between":[11,44],"users":[12,45],"to":[13,36,58],"disseminate":[14],"information":[15],"and":[16,40,79,99],"recruit":[17],"more":[18],"users.":[19],"The":[20],"temporal":[21],"evolution":[22],"of":[23,28,83,97,104,109],"adoption":[24],"or":[25,32],"cascade":[26,125],"process":[27],"new":[29],"products,":[30],"ideas":[33],"is":[34],"important":[35],"advertisers,":[37],"OSN":[38,112],"operators":[39],"application":[41],"developers.":[42],"Interactions":[43],"are":[46,73],"represented":[47],"by":[48],"massive":[49],"directed":[50],"graphs,":[51],"so":[52],"graph":[53,63],"sampling":[54,64,98],"methods":[55,121],"were":[56],"proposed":[57],"capture":[59],"their":[60],"properties.":[61],"Existing":[62],"methods,":[65],"such":[66],"as":[67],"a":[68,89,95,128],"simple":[69],"random":[70],"walk,":[71],"however,":[72],"ill-":[74],"suited":[75],"for":[76],"capturing":[77,105],"this":[78],"other":[80],"dynamic":[81],"properties":[82,108],"graph.":[85],"We":[86,114],"propose":[87],"ForestStream,":[88],"measurement":[90],"method":[91],"that":[92],"relies":[93],"combination":[96],"streaming":[100],"with":[101,127],"goal":[103],"statistical":[107],"cascades":[110],"in":[111,122],"graphs.":[113],"demonstrate":[115],"our":[116],"method's":[117],"accuracy":[118],"over":[119],"existing":[120],"inferring":[123],"statistics,":[126],"low":[129],"memory":[130],"usage.":[131]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}