{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T17:09:56Z","timestamp":1726506596262},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>In this demonstration, we present ATTENet, a novel visual analytic system for detecting and explaining suspicious affiliated-transaction-based tax evasion (ATTE) groups. First, the system constructs a taxpayer interest interacted network, which contains economic behaviors and social relationships between taxpayers. Then, the system combines basic features and structure features of each group in the network with network embedding method structure2Vec, and then detects suspicious ATTE groups with random forest algorithm. Last, to explore and explain the detection results, the system provides an ATTENet visualization with three coordinated views and interactive tools. We demonstrate ATTENet on a non-confidential dataset which contains two years of real tax data obtained by our cooperative tax authorities to verify the usefulness of our system.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/964","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"6584-6586","source":"Crossref","is-referenced-by-count":3,"title":["ATTENet: Detecting and Explaining Suspicious Tax Evasion Groups"],"prefix":"10.24963","author":[{"given":"Qinghua","family":"Zheng","sequence":"first","affiliation":[{"name":"MOE Key Laboratory of Intelligent Networks and Network Security, Xi'an Jiaotong University"},{"name":"School of Computer Science and Technology, Xi'an Jiaotong University"}]},{"given":"Yating","family":"Lin","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Intelligent Networks and Network Security, Xi'an Jiaotong University"},{"name":"School of Computer Science and Technology, Xi'an Jiaotong University"}]},{"given":"Huan","family":"He","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Intelligent Networks and Network Security, Xi'an Jiaotong University"},{"name":"School of Computer Science and Technology, Xi'an Jiaotong University"}]},{"given":"Jianfei","family":"Ruan","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Intelligent Networks and Network Security, Xi'an Jiaotong University"},{"name":"School of Computer Science and Technology, Xi'an Jiaotong University"}]},{"given":"Bo","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Continuing Education, Xi'an Jiaotong University"},{"name":"National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:53:05Z","timestamp":1564300385000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/964"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/964","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}