{"id":"https://openalex.org/W3092437298","doi":"https://doi.org/10.1145/3397166.3409130","title":"Iterative learning of graph connectivity from partially-observed cascade samples","display_name":"Iterative learning of graph connectivity from partially-observed cascade samples","publication_year":2020,"publication_date":"2020-10-08","ids":{"openalex":"https://openalex.org/W3092437298","doi":"https://doi.org/10.1145/3397166.3409130","mag":"3092437298"},"language":"en","primary_location":{"id":"doi:10.1145/3397166.3409130","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397166.3409130","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing","raw_type":"proceedings-article"},"type":"conference-paper","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/A5052349517","display_name":"Jiin Woo","orcid":null},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jiin Woo","raw_affiliation_strings":["NAVER Search, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NAVER Search, South Korea","institution_ids":["https://openalex.org/I60922564"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000550975","display_name":"Jungseul Ok","orcid":"https://orcid.org/0000-0003-4742-2473"},"institutions":[{"id":"https://openalex.org/I123900574","display_name":"Pohang University of Science and Technology","ror":"https://ror.org/04xysgw12","country_code":"KR","type":"education","lineage":["https://openalex.org/I123900574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jungseul Ok","raw_affiliation_strings":["POSTECH, AIGS/CSE, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"POSTECH, AIGS/CSE, South Korea","institution_ids":["https://openalex.org/I123900574"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037245619","display_name":"Yung Yi","orcid":"https://orcid.org/0000-0002-6955-3135"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yung Yi","raw_affiliation_strings":["KAIST, EE, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST, EE, South Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3021,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.812749,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"141","last_page":"150"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9973000288009644,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9829000234603882,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7550550699234009},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7186570763587952},{"id":"https://openalex.org/keywords/cascade","display_name":"Cascade","score":0.6664668917655945},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6370397806167603},{"id":"https://openalex.org/keywords/iterated-function","display_name":"Iterated function","score":0.5318120718002319},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4253005385398865},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40470319986343384},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38385623693466187},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38049209117889404},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1889629065990448}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7550550699234009},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7186570763587952},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.6664668917655945},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6370397806167603},{"id":"https://openalex.org/C140479938","wikidata":"https://www.wikidata.org/wiki/Q5254619","display_name":"Iterated function","level":2,"score":0.5318120718002319},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4253005385398865},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40470319986343384},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38385623693466187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38049209117889404},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1889629065990448},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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":2,"locations":[{"id":"doi:10.1145/3397166.3409130","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397166.3409130","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:oasis.postech.ac.kr:2014.oak/104572","is_oa":false,"landing_page_url":"https://oasis.postech.ac.kr/handle/2014.oak/104572","pdf_url":null,"source":{"id":"https://openalex.org/S4306401965","display_name":"Open Access System for Information Sharing (Pohang University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I123900574","host_organization_name":"Pohang University of Science and Technology","host_organization_lineage":["https://openalex.org/I123900574"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1984825171","https://openalex.org/W1996816151","https://openalex.org/W2061820396","https://openalex.org/W2067883080","https://openalex.org/W2092418988","https://openalex.org/W2104566743","https://openalex.org/W2110102971","https://openalex.org/W2131997358","https://openalex.org/W2369072273","https://openalex.org/W2912500072"],"related_works":["https://openalex.org/W2153719181","https://openalex.org/W1971748923","https://openalex.org/W2060986072","https://openalex.org/W1566155057","https://openalex.org/W3110732990","https://openalex.org/W3120641340","https://openalex.org/W64588465","https://openalex.org/W2117825986","https://openalex.org/W2079855347","https://openalex.org/W1995257921"],"abstract_inverted_index":{"Graph":[0],"learning":[1,58],"is":[2,110,127],"an":[3,62],"inference":[4,102,121],"problem":[5],"of":[6,9,15,25,43,70,88,113,138,141,147,175],"estimating":[7],"connectivity":[8],"a":[10,13,37,67,119,131],"graph":[11,57,95],"from":[12,158],"collection":[14],"epidemic":[16],"cascades,":[17],"with":[18],"many":[19],"useful":[20],"applications":[21],"in":[22,49,77,136,173],"the":[23,41,50,56,86,101,106,111,139,145],"areas":[24],"online/offline":[26],"social":[27,153],"networks,":[28,30,154],"p2p":[29],"computer":[31],"security,":[32],"and":[33,80,93,97,123,162],"epidemiology.":[34],"We":[35,104,143],"consider":[36],"practical":[38],"scenario":[39],"when":[40],"information":[42],"cascade":[44,52,115],"samples":[45,116],"are":[46],"partially":[47],"observed":[48],"independent":[51],"(IC)":[53],"model.":[54],"For":[55],"problem,":[59],"we":[60],"propose":[61],"efficient":[63],"algorithm":[64,84,149,166],"that":[65,125,164],"solves":[66],"localized":[68],"version":[69],"computationally-intractable":[71],"maximum":[72],"likelihood":[73],"estimation":[74],"through":[75],"approximations":[76],"both":[78],"temporal":[79],"spatial":[81],"aspects.":[82],"Our":[83],"iterates":[85],"operations":[87],"recovering":[89],"missing":[90],"time":[91],"logs":[92],"inferring":[94],"connectivity,":[96],"thereby":[98],"progressively":[99],"improves":[100],"quality.":[103],"study":[105],"sample":[107],"complexity,":[108],"which":[109],"number":[112,140],"required":[114],"to":[117,130,160],"meet":[118],"given":[120],"quality,":[122],"show":[124],"it":[126],"asymptotically":[128],"close":[129],"lower":[132],"bound,":[133],"thus":[134],"near-order-optimal":[135],"terms":[137,174],"nodes.":[142],"evaluate":[144],"performance":[146],"our":[148,165],"using":[150],"five":[151],"real-world":[152],"whose":[155],"size":[156],"ranges":[157],"20":[159],"900,":[161],"demonstrate":[163],"performs":[167],"better":[168],"than":[169],"other":[170],"competing":[171],"algorithms":[172],"accuracy":[176],"while":[177],"maintaining":[178],"fast":[179],"running":[180],"time.":[181]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}