Taxpayer tax evasion suspicion group detection method based on multi-stage MapReduce model

Abstract

The invention discloses a taxpayer tax evasion suspicion group detection method based on a multi-stage MapReduce model. The method comprises the following steps: firstly, proposing a method of constructing a taxpayer benefit association network based on a color-patch map through multi-network integration; secondly, proposing a reduction method of the taxpayer benefit association network based on strongly connected components; thirdly, introducing a concept of a benefit antecedent network, and finding all maximal weakly-connected sub-graphs in the benefit antecedent network; and lastly, constructing a pattern tree for the maximal weakly-connected sub-graphs and corresponding trade edges thereof through the multi-stage MapReduce model, traversing the pattern tree to generate a pattern library, matching patterns in the pattern library pairwise, finding all pattern pairs conforming to a matching principle, and finally generating all taxpayer evasion suspicion groups. Distributed calculation is adopted in the multi-stage MapReduce model, so that the suspicion tax evasion analysis efficiency of national tax departments can be increased greatly, and the national tax loss is recovered.

Description

Technical field [0001] The invention relates to a group detection method for taxpayers suspected of tax evasion based on a multi-stage MapReduce model. Background technique [0002] With the continuous development of information technology, the national tax informatization has initially established a unified integrated technical support and service platform for electronic declaration, tax payment, and approval, and generates massive tax data. However, the analysis of corporate tax evasion and evasion is manual analysis, and tax data is stored heterogeneously across regions, and it is difficult to find implicit interest relationships between taxpayers. [0003] The patent "A method for identifying tax evasion related companies based on the taxpayer's interest-related network model" (CN103383767B) uses colored weighted graphs for modeling, and gives five aggregation operators based on the colors of edges and nodes. Combine these aggregation operators to discover The taxpayer's inter...

AI Technical Summary

Problems solved by technology

[0005] The above existing taxpayer tax evasion suspect group detection methods have the following problems: 1. The constructed taxpayer interest-related network does not consider the problem of high graph processing time and space complexity caused by large-scale nodes and complex edge relationships; 2. .There is a pattern combination explosion problem in graph reduction and graph mining; 3. Patent CN104103011B is difficult to find non-motif structures (such as pentagons, hexagons, etc.)

The above three points lead to their inefficiency when dealing with large-scale graph data

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