# Solve fraud detection problem by using graph based learning methods

arXiv: https://arxiv.org/abs/1908.11708 · HTML: https://mlsanity.com/paper/1908.11708/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 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)

The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss of the credit cards' holders and the banks. To detect the credit cards' fraud transactions, data scientists normally employ the unsupervised learning techniques and supervised learning techniques. In this paper, we employ the graph p-Laplacian based semi-supervised learning methods combined with the undersampling techniques such as Cluster Centroids to solve the credit cards' fraud transactions detection problem. Experimental results show that the graph p-Laplacian semi-supervised learning methods outperform the current state of the art graph Laplacian based semi-supervised learning method (p=2).