{"id":"https://openalex.org/W2736225914","doi":"https://doi.org/10.1145/3091478.3091518","title":"ASSIST","display_name":"ASSIST","publication_year":2017,"publication_date":"2017-06-25","ids":{"openalex":"https://openalex.org/W2736225914","doi":"https://doi.org/10.1145/3091478.3091518","mag":"2736225914"},"language":"en","primary_location":{"id":"doi:10.1145/3091478.3091518","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3091478.3091518","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Web Science Conference","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/A5021442429","display_name":"Charalampos Chelmis","orcid":"https://orcid.org/0000-0002-5920-1451"},"institutions":[{"id":"https://openalex.org/I113508548","display_name":"Albany State University","ror":"https://ror.org/01vme4277","country_code":"US","type":"education","lineage":["https://openalex.org/I113508548"]},{"id":"https://openalex.org/I392282","display_name":"University at Albany, State University of New York","ror":"https://ror.org/012zs8222","country_code":"US","type":"education","lineage":["https://openalex.org/I392282"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charalampos Chelmis","raw_affiliation_strings":["University at Abany, State University of New York, Albany, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University at Abany, State University of New York, Albany, NY, USA","institution_ids":["https://openalex.org/I113508548","https://openalex.org/I392282"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111286053","display_name":"Reshul Dani","orcid":null},"institutions":[{"id":"https://openalex.org/I878213199","display_name":"Savitribai Phule Pune University","ror":"https://ror.org/044g6d731","country_code":"IN","type":"education","lineage":["https://openalex.org/I878213199"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Reshul Dani","raw_affiliation_strings":["College of Engineering Pune, Pune, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Engineering Pune, Pune, India","institution_ids":["https://openalex.org/I878213199"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.06234944,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"201","last_page":"205"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9993000030517578,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9993000030517578,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9983999729156494,"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/T10799","display_name":"Data Visualization and Analytics","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/sensemaking","display_name":"Sensemaking","score":0.8330476880073547},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7835286259651184},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.6565243005752563},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.6501222252845764},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6381278038024902},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6287943124771118},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5253164768218994},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.4211377203464508},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4101969003677368},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2964732348918915},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.2225097119808197},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.16292792558670044}],"concepts":[{"id":"https://openalex.org/C2780554381","wikidata":"https://www.wikidata.org/wiki/Q2063340","display_name":"Sensemaking","level":2,"score":0.8330476880073547},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7835286259651184},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.6565243005752563},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.6501222252845764},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6381278038024902},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6287943124771118},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5253164768218994},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.4211377203464508},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4101969003677368},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2964732348918915},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2225097119808197},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.16292792558670044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3091478.3091518","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3091478.3091518","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Web Science Conference","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/W165525898","https://openalex.org/W1904633530","https://openalex.org/W1964669181","https://openalex.org/W1989993068","https://openalex.org/W1994727615","https://openalex.org/W2029852131","https://openalex.org/W2032280284","https://openalex.org/W2054658115","https://openalex.org/W2089554624","https://openalex.org/W2108781142","https://openalex.org/W2114353347","https://openalex.org/W2122646361","https://openalex.org/W2124499489","https://openalex.org/W2125050594","https://openalex.org/W2137130182","https://openalex.org/W2148123869","https://openalex.org/W2167295942","https://openalex.org/W4214926737"],"related_works":["https://openalex.org/W3002559787","https://openalex.org/W2100609754","https://openalex.org/W2050640900","https://openalex.org/W2049050102","https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W1598471830","https://openalex.org/W3107369729"],"abstract_inverted_index":{"Detecting":[0],"outliers":[1],"and":[2,19,80,92,160,171],"anomalies":[3],"in":[4,8,11,45,68,110,127],"data":[5,73,76],"is":[6,88],"vital":[7],"numerous":[9],"applications":[10],"areas":[12],"such":[13,65,86],"as":[14,29],"security,":[15],"finance,":[16],"health":[17],"care,":[18],"online":[20],"social":[21],"media.":[22],"Such":[23],"dynamic":[24],"systems":[25],"can":[26],"be":[27],"modeled":[28],"graphs":[30],"that":[31,143],"change":[32],"over":[33],"time.":[34],"Even":[35],"though":[36],"considerable":[37],"work":[38,57],"has":[39,58],"been":[40,59],"performed":[41],"on":[42,61,151],"finding":[43],"points":[44],"time":[46],"at":[47],"which":[48],"a":[49,102,138],"network":[50,132],"notably":[51],"differs":[52],"from":[53],"its":[54],"past,":[55],"little":[56],"done":[60],"characterizing":[62],"or":[63],"explaining":[64],"changes.":[66],"However,":[67],"the":[69,122,128,131,147,162,178],"era":[70],"of":[71,130,140,164],"big":[72],"where":[74],"networked":[75],"are":[77],"getting":[78],"bigger":[79],"bigger,":[81],"being":[82],"able":[83],"to":[84,105,120,135,146,167,175],"summarize":[85,106],"changes":[87,109,126,170],"key":[89,141],"for":[90],"sensemaking":[91],"root":[93],"cause":[94],"analysis.":[95],"To":[96],"address":[97],"this":[98],"gap,":[99],"we":[100,115],"present":[101],"novel":[103],"approach":[104,119,166],"significant":[107],"structural":[108],"large":[111],"temporal":[112],"graphs.":[113],"Specifically,":[114],"propose":[116],"an":[117],"efficient":[118],"help":[121],"user":[123],"understand":[124],"sharp":[125],"structure":[129],"by":[133],"presenting":[134],"her":[136],"only":[137],"summary":[139],"subgraphs":[142],"contribute":[144],"most":[145],"change.":[148,179],"Extensive":[149],"evaluation":[150],"real-world":[152],"datasets":[153],"with":[154],"ground":[155],"truth":[156],"demonstrates":[157],"both":[158],"quantitatively":[159],"qualitatively":[161],"ability":[163],"our":[165],"accurately":[168],"detect":[169],"discover":[172],"\"important\"":[173],"structures":[174],"succinctly":[176],"describe":[177]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2017-07-21T00:00:00"}