# Spectrum-based deep neural networks for fraud detection

arXiv: https://arxiv.org/abs/1706.00891 · HTML: https://mlsanity.com/paper/1706.00891/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2017.

## Built on by (most cited first, 3 of 3)

- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [Itsy Bitsy SpiderNet: Fully Connected Residual Network for Fraud Detection](https://mlsanity.com/paper/2105.08120/) (2021)
- [Enhancement to Training of Bidirectional GAN : An Approach to Demystify Tax Fraud](https://mlsanity.com/paper/2208.07675/) (2022)

## Abstract (arXiv)

In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spectral graph analysis. In particular, we use the node projection (called as spectral coordinate) in the low dimensional spectral space of the graph's adjacency matrix as input of deep neural networks. Spectral coordinates in the spectral space capture the most useful topology information of the network. Due to the small dimension of spectral coordinates (compared with the dimension of the adjacency matrix derived from a graph), training deep neural networks becomes feasible. We develop and evaluate two neural networks, deep autoencoder and convolutional neural network, in our fraud detection framework. Experimental results on a real signed graph show that our spectrum based deep neural networks are effective in fraud detection.