{"id":"https://openalex.org/W3093782407","doi":null,"title":"Early Anomaly Detection by Learning and Forecasting Behavior.","display_name":"Early Anomaly Detection by Learning and Forecasting Behavior.","publication_year":2020,"publication_date":"2020-10-20","ids":{"openalex":"https://openalex.org/W3093782407","mag":"3093782407"},"language":"en","primary_location":{"id":"mag:3093782407","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2010.10016","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://export.arxiv.org/pdf/2010.10016","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035766567","display_name":"Tong Zhao","orcid":"https://orcid.org/0000-0001-7660-1732","observed_orcids":["https://orcid.org/0000-0001-7660-1732","https://orcid.org/0009-0002-4937-7880"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tong Zhao","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-7660-1732","affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Bo Ni","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bo Ni","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028597555","display_name":"Wenhao Yu","orcid":"https://orcid.org/0000-0002-4075-5980","observed_orcids":["https://orcid.org/0000-0002-4075-5980","https://orcid.org/0009-0002-1871-272X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenhao Yu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4075-5980","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5074821819","display_name":"Meng Jiang","orcid":"https://orcid.org/0000-0002-3009-519X","observed_orcids":["https://orcid.org/0000-0002-3009-519X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng Jiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.8583999872207642,"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":0.8583999872207642,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.07320000231266022,"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/T11644","display_name":"Spam and Phishing Detection","score":0.01269999984651804,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/graph-anomaly-detection","display_name":"graph anomaly detection","score":0.9240000247955322},{"id":"https://openalex.org/keywords/early-anomaly-detection","display_name":"early anomaly detection","score":0.9890000224113464},{"id":"https://openalex.org/keywords/behavior-prediction","display_name":"behavior prediction","score":0.9089999794960022},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.8330000042915344},{"id":"https://openalex.org/keywords/behavior-graphs","display_name":"behavior graphs","score":0.6039999723434448},{"id":"https://openalex.org/keywords/sequence-to-sequence","display_name":"Seq2Seq","score":0.5580000281333923},{"id":"https://openalex.org/keywords/online-social-networks","display_name":"social networking sites","score":0.4749999940395355},{"id":"https://openalex.org/keywords/e-commerce-platforms","display_name":"e-commerce platforms","score":0.7519999742507935},{"id":"https://openalex.org/keywords/eland","display_name":"eland","score":0.699999988079071}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7834490537643433},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6578035950660706},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5830955505371094},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5814028978347778},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5299117565155029},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43229883909225464},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43036291003227234},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39998558163642883},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.16757714748382568},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0877220630645752},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3093782407","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2010.10016","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3093782407","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2010.10016","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1560607100","https://openalex.org/W2011863672","https://openalex.org/W2064058256","https://openalex.org/W2064675550","https://openalex.org/W2101447063","https://openalex.org/W2120797124","https://openalex.org/W2130942839","https://openalex.org/W2132339004","https://openalex.org/W2182891243","https://openalex.org/W2250539671","https://openalex.org/W2282288858","https://openalex.org/W2286850974","https://openalex.org/W2337240321","https://openalex.org/W2348679751","https://openalex.org/W2406128552","https://openalex.org/W2468907370","https://openalex.org/W2513234781","https://openalex.org/W2528820915","https://openalex.org/W2547875792","https://openalex.org/W2565330852","https://openalex.org/W2610034660","https://openalex.org/W2618170429","https://openalex.org/W2624431344","https://openalex.org/W2739251211","https://openalex.org/W2783466287","https://openalex.org/W2792307011","https://openalex.org/W2798795235","https://openalex.org/W2807021761","https://openalex.org/W2809409545","https://openalex.org/W2809418595","https://openalex.org/W2896429681","https://openalex.org/W2903345182","https://openalex.org/W2912869854","https://openalex.org/W2944250323","https://openalex.org/W2954490019","https://openalex.org/W2963017945","https://openalex.org/W2965857891","https://openalex.org/W2990045899","https://openalex.org/W2998313947","https://openalex.org/W3014563232","https://openalex.org/W3016259256","https://openalex.org/W3034213836","https://openalex.org/W3035298482","https://openalex.org/W3045255111","https://openalex.org/W3093649180","https://openalex.org/W3101913790"],"related_works":["https://openalex.org/W3198839083","https://openalex.org/W4378213900","https://openalex.org/W2963932720","https://openalex.org/W4404792608","https://openalex.org/W3161896327","https://openalex.org/W7208770034","https://openalex.org/W4285612255","https://openalex.org/W2740367800","https://openalex.org/W4416416344","https://openalex.org/W4392384285"],"abstract_inverted_index":{"Graph":[0],"anomaly":[1,89,133,148,178,190],"detection":[2,149,157,179,191],"systems":[3],"aim":[4],"at":[5,21,69,192],"identifying":[6],"suspicious":[7],"accounts":[8],"or":[9,108,199],"behaviors":[10,143],"on":[11,204],"social":[12],"networking":[13],"sites":[14],"and":[15,116,131,155],"e-commercial":[16],"platforms.":[17],"Detecting":[18],"anomalous":[19],"users":[20],"an":[22,109,193],"early":[23,71,88],"stage":[24,195],"is":[25,138,196],"crucial":[26],"to":[27,52],"minimize":[28],"financial":[29],"loss.":[30,55],"When":[31],"a":[32,79,93,101,105,174,205],"great":[33],"amount":[34,207],"of":[35,173,176,189,208],"observed":[36,64],"behavior":[37,84,95,129,136,162],"data":[38,65,85],"are":[39,66],"available,":[40],"existing":[41],"methods":[42,150,184,203],"perform":[43],"effectively":[44],"though":[45],"it":[46],"may":[47],"have":[48],"been":[49,113],"too":[50],"late":[51],"avoid":[53],"the":[54,63,70,125,141,146,156,161,171,182,187],"However,":[56],"their":[57],"performance":[58,172,188],"would":[59],"become":[60],"unsatisfactory":[61],"when":[62],"quite":[67],"limited":[68],"stage.":[72],"In":[73],"this":[74],"work,":[75],"we":[76],"propose":[77],"Eland,":[78,186],"novel":[80],"framework":[81],"that":[82,97,111,145,168],"uses":[83],"augmentation":[86],"for":[87],"detection.":[90,134],"It":[91],"has":[92,112],"Seq2Seq-based":[94],"predictor":[96,163],"predicts":[98],"(i)":[99],"whether":[100],"user":[102],"will":[103,120],"adopt":[104],"new":[106],"item":[107,110,119],"historically":[114],"adopted":[115],"(ii)":[117],"which":[118],"be":[121],"adopted.":[122],"Eland":[123,169],"exploits":[124],"mutual":[126],"enhancement":[127],"between":[128],"prediction":[130],"graph":[132,137],"The":[135],"augmented":[139,183],"with":[140,198],"predicted":[142],"such":[144],"graph-based":[147,177],"can":[151,159],"achieve":[152],"better":[153,200],"performance,":[154],"results":[158],"support":[160],"in":[164,185],"return.":[165],"Experiments":[166],"show":[167],"improves":[170],"variety":[175],"methods.":[180],"With":[181],"earlier":[194],"comparable":[197],"than":[201],"non-augmented":[202],"greater":[206],"observation.":[209]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2020-10-29T00:00:00"}