{"type":"ScholarlyArticle","title":"ATTENet: Detecting and Explaining Suspicious Tax Evasion Groups","authors":[{"name":"Qinghua Zheng","slug":"qinghua-zheng","given":"Qinghua","family":"Zheng","affiliation":"Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech; School of Computer Science and Technology , Xi'an Jiaotong University"},{"name":"Yating Lin","slug":"yating-lin","given":"Yating","family":"Lin","affiliation":""},{"name":"Huan He","slug":"huan-he","given":"Huan","family":"He","affiliation":"School of Continuing Education , Xi'an Jiaotong University; National Engineering Lab for Big Data Analytics , Xi'an Jiaotong University"},{"name":"Jianfei Ruan","slug":"jianfei-ruan","given":"Jianfei","family":"Ruan","affiliation":"Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech; School of Computer Science and Technology , Xi'an Jiaotong University"},{"name":"Bo Dong","slug":"bo-dong","given":"Bo","family":"Dong","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2019/964","url":"https://ijcai.org/proceedings/2019/964","pdf":"https://ijcai.org/proceedings/2019/0964.pdf","markdown":"https://ijcai.org/proceedings/2019/964.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/964","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Demos","track":"Demos","number":964,"pages":{"first":6584,"last":6586},"keywords":["AI: Human-Computer Interactive Systems","AI: Knowledge Representation and Reasoning","Applications: Finance","AI: Applications"],"videos":[],"references":[{"position":1,"raw":"[Balafoutas et al., 2015] Loukas Balafoutas, Adrian Beck, Rudolf Kerschbamer, and Matthias Sutter. The hidden costs of tax evasion.: Collaborative tax evasion in markets for expert services. Journal of Public Economics, 129:14- 25, September 2015.","title":"The hidden costs of tax evasion.: Collaborative tax evasion in markets for expert services","authors":"Loukas Balafoutas, Adrian Beck, Rudolf Kerschbamer, Matthias Sutter","year":"2015","venue":"Journal of Public Economics","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Didimo et al., 2018] Walter Didimo, Luca Giamminonni, Giuseppe Liotta, Fabrizio Montecchiani, and Daniele Pagliuca. A visual analytics system to support tax evasion discovery. Decision Support Systems, 110:71-83, June 2018.","title":"A visual analytics system to support tax evasion discovery","authors":"Walter Didimo, Luca Giamminonni, Giuseppe Liotta, Fabrizio Montecchiani, Daniele Pagliuca","year":"2018","venue":"Decision Support Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ferrantino et al., 2012] Michael J. Ferrantino, Xuepeng Liu, and Zhi Wang. Evasion behaviors of exporters and importers: Evidence from the U.S.-China trade data dis- crepancy. Journal of International Economics, 86(1):141- 157, January 2012.","title":"Evasion behaviors of exporters and importers: Evidence from the U.S.-China trade data discrepancy","authors":"J Michael, Xuepeng Ferrantino, Zhi Liu, Wang","year":"2012","venue":"Journal of International Economics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Goumagias et al., 2018] Nikolaos D. Goumagias, Dimitrios Hristu-Varsakelis, and Yannis M. Assael. Using deep Q- learning to understand the tax evasion behavior of risk- averse firms. Expert Systems with Applications, 101:258- 270, July 2018.","title":"Using deep Qlearning to understand the tax evasion behavior of riskaverse firms","authors":"D Nikolaos, Dimitrios Goumagias, Yannis M Hristu-Varsakelis, Assael","year":"2018","venue":"Expert Systems with Applications","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Ruan et al., 2019] Jianfei Ruan, Zheng Yan, Bo Dong, Qinghua Zheng, and Buyue Qian. Identifying suspicious groups of affiliated-transaction-based tax evasion in big data. Information Sciences, 477:508-532, March 2019.","title":"Identifying suspicious groups of affiliated-transaction-based tax evasion in big data","authors":"Zheng Jianfei Ruan, Bo Yan, Qinghua Dong, Buyue Zheng, Qian","year":"2019","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Tian et al., 2016] F. Tian, T. Lan, K. Chao, N. Godwin, Q. Zheng, N. Shah, and F. Zhang. Mining Suspicious Tax Evasion Groups in Big Data. IEEE Transactions on Knowledge and Data Engineering, 28(10):2651-2664, October 2016.","title":"Mining Suspicious Tax Evasion Groups in Big Data","authors":"F Tian, T Lan, K Chao, N Godwin, Q Zheng, N Shah, F Zhang","year":"2016","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}