  To detect the irregular trade behaviors in the stock market is the important
problem in machine learning field. These irregular trade behaviors are
obviously illegal. To detect these irregular trade behaviors in the stock
market, data scientists normally employ the supervised learning techniques. In
this paper, we employ the three graph Laplacian based semi-supervised ranking
methods to solve the irregular trade behavior detection problem. Experimental
results show that that the un-normalized and symmetric normalized graph
Laplacian based semi-supervised ranking methods outperform the random walk
Laplacian based semi-supervised ranking method.
