Eigenspace analysis for threat detection in social networks

  title={Eigenspace analysis for threat detection in social networks},
  author={Benjamin A. Miller and Michelle S. Beard and Nadya T. Bliss},
  journal={14th International Conference on Information Fusion},
The problem of detecting a small, anomalous subgraph within a large background network is important and applicable to many fields. The non-Euclidean nature of graph data, however, complicates the application of classical detection theory in this context. A recent statistical framework for anomalous subgraph detection uses spectral properties of a graph's modularity matrix to determine the presence of an anomaly. In this paper, this detection framework and the related algorithms are applied to… CONTINUE READING
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