Efficient anomaly detection in dynamic, attributed graphs: Emerging phenomena and big data

@article{Miller2013EfficientAD,
  title={Efficient anomaly detection in dynamic, attributed graphs: Emerging phenomena and big data},
  author={Benjamin A. Miller and Nicholas Arcolano and Nadya T. Bliss},
  journal={2013 IEEE International Conference on Intelligence and Security Informatics},
  year={2013},
  pages={179-184}
}
When working with large-scale network data, the interconnected entities often have additional descriptive information. This additional metadata may provide insight that can be exploited for detection of anomalous events. In this paper, we use a generalized linear model for random attributed graphs to model connection probabilities using vertex metadata. For a class of such models, we show that an approximation to the exact model yields an exploitable structure in the edge probabilities… CONTINUE READING