{"id":"https://openalex.org/W3141702300","doi":"https://doi.org/10.1109/asonam49781.2020.9381363","title":"Forecasting Transactional Amount in Bitcoin Network Using Temporal GNN Approach","display_name":"Forecasting Transactional Amount in Bitcoin Network Using Temporal GNN Approach","publication_year":2020,"publication_date":"2020-12-07","ids":{"openalex":"https://openalex.org/W3141702300","doi":"https://doi.org/10.1109/asonam49781.2020.9381363","mag":"3141702300"},"language":"en","primary_location":{"id":"doi:10.1109/asonam49781.2020.9381363","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asonam49781.2020.9381363","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","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/A5087170533","display_name":"Shakshi Sharma","orcid":"https://orcid.org/0000-0001-8091-0781"},"institutions":[{"id":"https://openalex.org/I56085075","display_name":"University of Tartu","ror":"https://ror.org/03z77qz90","country_code":"EE","type":"education","lineage":["https://openalex.org/I56085075"]}],"countries":["EE"],"is_corresponding":true,"raw_author_name":"Shakshi Sharma","raw_affiliation_strings":["Institute of Computer Science, University of Tartu,Estonia"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Science, University of Tartu,Estonia","institution_ids":["https://openalex.org/I56085075"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011512226","display_name":"Rajesh Sharma","orcid":"https://orcid.org/0000-0003-3581-1332"},"institutions":[{"id":"https://openalex.org/I56085075","display_name":"University of Tartu","ror":"https://ror.org/03z77qz90","country_code":"EE","type":"education","lineage":["https://openalex.org/I56085075"]}],"countries":["EE"],"is_corresponding":false,"raw_author_name":"Rajesh Sharma","raw_affiliation_strings":["Institute of Computer Science, University of Tartu,Estonia"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Science, University of Tartu,Estonia","institution_ids":["https://openalex.org/I56085075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5087170533"],"corresponding_institution_ids":["https://openalex.org/I56085075"],"apc_list":null,"apc_paid":null,"fwci":1.0676,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.84965479,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"478","last_page":"485"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10270","display_name":"Blockchain Technology Applications and Security","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T10270","display_name":"Blockchain Technology Applications and Security","score":0.9994000196456909,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9882000088691711,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7582051157951355},{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.7257089614868164},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6243169903755188},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6144999265670776},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6108207702636719},{"id":"https://openalex.org/keywords/transaction-data","display_name":"Transaction data","score":0.5525446534156799},{"id":"https://openalex.org/keywords/transactional-leadership","display_name":"Transactional leadership","score":0.5495733022689819},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.5135841369628906},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45764464139938354},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4549751281738281},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.44513165950775146},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4149208664894104},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.40453478693962097},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3964012861251831},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3670145869255066},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.355745404958725},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.15029898285865784},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.14072859287261963},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1402004361152649},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1095009446144104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7582051157951355},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.7257089614868164},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6243169903755188},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6144999265670776},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6108207702636719},{"id":"https://openalex.org/C127722929","wikidata":"https://www.wikidata.org/wiki/Q7833714","display_name":"Transaction data","level":3,"score":0.5525446534156799},{"id":"https://openalex.org/C68489960","wikidata":"https://www.wikidata.org/wiki/Q2370659","display_name":"Transactional leadership","level":2,"score":0.5495733022689819},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.5135841369628906},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45764464139938354},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4549751281738281},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.44513165950775146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4149208664894104},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.40453478693962097},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3964012861251831},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3670145869255066},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.355745404958725},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.15029898285865784},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.14072859287261963},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1402004361152649},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1095009446144104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asonam49781.2020.9381363","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asonam49781.2020.9381363","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","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":50,"referenced_works":["https://openalex.org/W179922057","https://openalex.org/W1512620075","https://openalex.org/W1980458013","https://openalex.org/W2012079387","https://openalex.org/W2015174807","https://openalex.org/W2030714694","https://openalex.org/W2093015399","https://openalex.org/W2094165287","https://openalex.org/W2122878634","https://openalex.org/W2186693638","https://openalex.org/W2286029546","https://openalex.org/W2489294643","https://openalex.org/W2519887557","https://openalex.org/W2519935277","https://openalex.org/W2579433393","https://openalex.org/W2584978782","https://openalex.org/W2741908740","https://openalex.org/W2762509488","https://openalex.org/W2765776117","https://openalex.org/W2766355270","https://openalex.org/W2773450684","https://openalex.org/W2790916638","https://openalex.org/W2802435027","https://openalex.org/W2885628225","https://openalex.org/W2891695188","https://openalex.org/W2891870391","https://openalex.org/W2898373245","https://openalex.org/W2900967578","https://openalex.org/W2901504064","https://openalex.org/W2905573247","https://openalex.org/W2925436546","https://openalex.org/W2963328115","https://openalex.org/W2964015378","https://openalex.org/W2998313947","https://openalex.org/W3008568436","https://openalex.org/W3092685486","https://openalex.org/W3099213780","https://openalex.org/W3121630505","https://openalex.org/W3121889917","https://openalex.org/W3125025305","https://openalex.org/W3149599830","https://openalex.org/W3151748982","https://openalex.org/W4238624051","https://openalex.org/W4248175462","https://openalex.org/W4402305963","https://openalex.org/W6630831104","https://openalex.org/W6686748917","https://openalex.org/W6757370834","https://openalex.org/W6793285265","https://openalex.org/W6871647969"],"related_works":["https://openalex.org/W4320483443","https://openalex.org/W4205958290","https://openalex.org/W3195168932","https://openalex.org/W2979979539","https://openalex.org/W4311106074","https://openalex.org/W3127425528","https://openalex.org/W3004897296","https://openalex.org/W4292148089","https://openalex.org/W3211546796","https://openalex.org/W3141702300"],"abstract_inverted_index":{"Financial":[0],"institutions":[1],"such":[2,168],"as":[3,132],"banks":[4,25,32],"regularly":[5],"forecast":[6],"the":[7,19,39,49,55,73,107,151],"amount":[8,56,108],"of":[9,41,95,109,153,170],"finances":[10],"an":[11,164],"individual":[12],"will":[13,59],"have":[14],"in":[15,18,26],"his/her":[16,62],"account":[17],"near":[20],"future.":[21],"This":[22,87],"can":[23,33],"help":[24],"categorizing":[27],"their":[28,42],"customers":[29],"so":[30],"that":[31,37,161],"recommend":[34],"financial":[35,51],"products":[36],"matches":[38],"needs":[40],"customers.":[43],"In":[44,69,156],"this":[45],"work,":[46],"we":[47,71],"explored":[48],"historical":[50],"transactions":[52],"for":[53,105,167],"predicting":[54,106],"a":[57,66,92,113,116],"customer":[58,114],"receive":[60],"through":[61],"transacting":[63],"partners":[64],"at":[65,115],"specific":[67,93],"time.":[68],"particular,":[70],"use":[72],"Bitcoin":[74],"transactional":[75],"dataset,":[76],"which":[77],"has":[78],"two":[79],"main":[80],"characteristics:":[81],"i)":[82],"network,":[83],"and":[84],"ii)":[85],"temporal.":[86],"paper":[88],"contributes":[89],"by":[90,112],"exploiting":[91],"kind":[94,169],"Graph":[96],"Neural":[97],"Network":[98,103],"approach":[99,125],"called":[100],"Temporal-Graph":[101],"Convolutional":[102],"(T-GCN)":[104],"Bitcoins":[110],"received":[111],"particular":[117],"timestamp.":[118],"The":[119],"lower":[120],"errors":[121],"obtained":[122],"using":[123],"T-GCN":[124,154],"compared":[126],"to":[127],"11":[128],"baseline":[129],"approaches":[130],"(such":[131],"Support":[133],"Vector":[134,141],"Regression":[135,139],"(SVR),":[136],"Random":[137],"Forest":[138],"(RFR),":[140],"Auto-Regressive":[142],"(VAR),":[143],"Long":[144],"Short-Term":[145],"Memory":[146],"(LSTM),":[147],"etc.)":[148],"clearly":[149],"demonstrate":[150],"effectiveness":[152],"approach.":[155],"addition,":[157],"our":[158],"findings":[159],"reveal":[160],"time":[162],"is":[163],"important":[165],"feature":[166],"predictive":[171],"tasks.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}