  Tax evasion is a serious economic problem for many countries, as it can
undermine the government' s tax system and lead to an unfair business
competition environment. Recent research has applied data analytics techniques
to analyze and detect tax evasion behaviors of individual taxpayers. However,
they failed to support the analysis and exploration of the uprising related
party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where
a group of taxpayers is involved. In this paper, we present TaxThemis, an
interactive visual analytics system to help tax officers mine and explore
suspicious tax evasion groups through analyzing heterogeneous tax-related data.
A taxpayer network is constructed and fused with the trade network to detect
suspicious RPTTE groups. Rich visualizations are designed to facilitate the
exploration and investigation of suspicious transactions between related
taxpayers with profit and topological data analysis. Specifically, we propose a
calendar heatmap with a carefully-designed encoding scheme to intuitively show
the evidence of transferring revenue through related party transactions. We
demonstrate the usefulness and effectiveness of TaxThemis through two case
studies on real-world tax-related data, and interviews with domain experts.
