Spectrum-based Deep Neural Networks for Fraud Detection
Document Type
Article - Abstract Only
Publication Date
2017
Keywords
fraud detection, spectrum, deep neural networks
Abstract
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 the 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.
Citation
Yuan, S., Wu, X., Li, J., & Lu, A. (2017). (2017). Spectrum-based deep neural networks for fraud detection. Paper presented at the Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore, Singapore. 2419-2422. doi:10.1145/3132847.3133139
Comments
Principal Investigator: Xintao Wu
Acknowledgements: The authors acknowledge the support from the 973 Program of China (2014CB340404) to Shuhan Yuan and from National Science Foundation to Xintao Wu (1564250), Jun Li (1564348) and Aidong Lu (1564039). This research was conducted while Shuhan Yuan visited University of Arkansas.