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To detect the irregular trade behaviors in the stock market is the importantproblem in machine learning field. These irregular trade behaviors areobviously illegal. To detect these irregular trade behaviors in the stockmarket, data scientists normally employ the supervised learning techniques. Inthis paper, we employ the three graph Laplacian based semi-supervised rankingmethods to solve the irregular trade behavior detection problem. Experimentalresults show that that the un-normalized and symmetric normalized graphLaplacian based semi-supervised ranking methods outperform the random walkLaplacian based semi-supervised ranking method.

To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods