Mining suspicious tax evasion groups in a corporate governance network

Wenda Wei, Zheng Yan, Jianfei Ruan, Qinghua Zheng, Bo Dong*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)


There is a new tendency for corporations to evade tax via Interest Affiliated Transactions (IAT) that are controlled by a potential “Guanxi” between the corporations’ controllers. At the same time, the taxation data is a classic kind of big data. These issues challenge the effectiveness of traditional data mining-based tax evasion detection methods. To address this problem, we first coin a definition of controller interlock, which characterizes the interlocking relationship between corporations’ controllers. Next, we present a colored and weighted network-based model for characterizing economic behaviors, controller interlock and other relationships, and IATs between corporations, and generate a heterogeneous information network-corporate governance network. Then, we further propose a novel Graph-based Suspicious Groups of Interlock based tax evasion Identification method, named GSG2I, which mainly consists of two steps: controller interlock pattern recognition and suspicious group identification. Experimental tests based on a real-world 7-year period tax data of one province in China, demonstrate that the GSG2I method can greatly improve the efficiency of tax evasion detection.

Original languageEnglish
Title of host publicationAlgorithms and Architectures for Parallel Processing - 17th International Conference, ICA3PP 2017, Proceedings
Number of pages11
Volume10393 LNCS
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Algorithms and Architectures for Parallel Processing - Helsinki, Finland
Duration: 21 Aug 201723 Aug 2017
Conference number: 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10393 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


ConferenceInternational Conference on Algorithms and Architectures for Parallel Processing
Abbreviated titleICA3PP


  • Big data
  • Controller interlock
  • Corporate governance network
  • Tax evasion

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