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

arXiv: https://arxiv.org/abs/1909.08964 · HTML: https://mlsanity.com/paper/1909.08964/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Compute the edge p-Laplacian centrality for air traffic network](https://mlsanity.com/paper/2512.14749/) (2025)

## Abstract (arXiv)

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.