EAGER: Spectral Analysis for Fraud Detection in Large-scale Networks
Date:
This project takes a unified spectral transformation approach to address challenges of analyzing network topology and identifying fraud patterns in large-scale dynamic networks by using data spectral transformation with network topology visualization. Large-scale social and communication networks contain rich topological information embedded inside, in addition to various structured, semi-structured, and unstructured data. The research is characterizing patterns of various attacks in the spectral projection space of graph topology and developing spectrum based methods to identify these attacks. The approach, which exploits the spectral space of the underlying interaction structure of the network, is orthogonal to traditional approaches using content profiling. The ability to perform this spectral analysis is dependent upon the development of complex mathematical techniques. Critical issues that are being explored include the scalability of the methods to very large data sets, the determination of the dimensionality of the node representation in spectral space, and the interpretation of patterns in spectral space.