This article presents fraudulent activity that are done by the unscrupulous desire of people to make the personal benefits by manipulating the tax in taxing system. Taxpayers manipulate the money paid to the tax authorities through avoidance and evasion activities. In this paper, we deal with a specific technique used by the tax-evaders known as a circular trading. We define an algorithm for detection and analysis of circular trade. To detect these circular trade, we have modeled whole system as a directed graph with actors being vertices and the transactions among them as directed edges. We have proposed an algorithm for detecting these circular trade. The commercial tax dataset is given by Telangana, India. This dataset contains the transaction details of participants involved in a known circular trade.
In @cite_0 , a graph clustering algorithm is devised for detecting collusion sets in stock markets. A novel feature of this approach is the use of Dempster–Schafer theory of evidence to combine the candidate collusion sets. In @cite_2 , a method is proposed to detect the potential collusive cliques involved in an instrument of future markets. In @cite_14 , the authors introduced complicity functions, which are capable of identifying the intermediaries in a group of actors, avoiding core elements that have nothing to do with the group.
@cite_0 @cite_14 @cite_2
2016647770 2011153821 1976160665 1613681423
Many mal-practices in stock market trading--e.g., circular trading and price manipulation--use the modus operandi of collusion. Informally, a set of traders is a candidate collusion set when they have "heavy trading" among themselves, as compared to their trading with others. We formalize the problem of detection of collusion sets, if any, in the given trading database. We show that naive approaches are inefficient for real-life situations. We adapt and apply two well-known graph clustering algorithms for this problem. We also propose a new graph clustering algorithm, specifically tailored for detecting collusion sets. A novel feature of our approach is the use of Dempster---Schafer theory of evidence to combine the candidate collusion sets detected by individual algorithms. Treating individual experiments as evidence, this approach allows us to quantify the confidence (or belief) in the candidate collusion sets. We present detailed simulation experiments to demonstrate effectiveness of the proposed algorithms. In financial markets, abnormal trading behaviors pose a serious challenge to market surveillance and risk management. What is worse, there is an increasing emergence of abnormal trading events that some experienced traders constitute a collusive clique and collaborate to manipulate some instruments, thus mislead other investors by applying similar trading behaviors for maximizing their personal benefits. In this paper, a method is proposed to detect the potential collusive cliques involved in an instrument of future markets by first calculating the correlation coefficient between any two eligible unified aggregated time series of signed order volume, and then combining the connected components from multiple sparsified weighted graphs constructed by using the correlation matrices where each correlation coefficient is over a user-specified threshold. Experiments conducted on real order data from the Shanghai Futures Exchange show that the proposed method can effectively detect suspect collusive cliques, which have been verified by financial experts. A tool based on the proposed method has been deployed in the exchange as a pilot application for futures market surveillance and risk management. Graph clustering has been widely applied in exploring regularities emerging in relational data. Recently, the rapid development of network theory correlates graph clustering with the detection of community structure, a common and important topological characteristic of networks. Most existing methods investigate the community structure at a single topological scale. However, as shown by empirical studies, the community structure of real world networks often exhibits multiple topological descriptions, corresponding to the clustering at different resolutions. Furthermore, the detection of multiscale community structure is heavily affected by the heterogeneous distribution of node degree. It is very challenging to detect multiscale community structure in heterogeneous networks. In this paper, we propose a novel, unified framework for detecting community structure from the perspective of dimensionality reduction. Based on the framework, we first prove that the well-known Laplacian matrix for network partition and the widely-used modularity matrix for community detection are two kinds of covariance matrices used in dimensionality reduction. We then propose a novel method to detect communities at multiple topological scales within our framework. We further show that existing algorithms fail to deal with heterogeneous node degrees. We develop a novel method to handle heterogeneity of networks by introducing a rescaling transformation into the covariance matrices in our framework. Extensive tests on real world and artificial networks demonstrate that the proposed correlation matrices significantly outperform Laplacian and modularity matrices in terms of their ability to identify multiscale community structure in heterogeneous networks. This paper studies the problem of detecting the presence of a small dense community planted in a large Erd o s-R 'enyi random graph @math , where the edge probability within the community exceeds @math by a constant factor. Assuming the hardness of the planted clique detection problem, we show that the computational complexity of detecting the community exhibits the following phase transition phenomenon: As the graph size @math grows and the graph becomes sparser according to @math , there exists a critical value of @math , below which there exists a computationally intensive procedure that can detect far smaller communities than any computationally efficient procedure, and above which a linear-time procedure is statistically optimal. The results also lead to the average-case hardness results for recovering the dense community and approximating the densest @math -subgraph.