Goodness-of-fit statistics for anomaly detection in Chung-Lu random graphs

  title={Goodness-of-fit statistics for anomaly detection in Chung-Lu random graphs},
  author={Benjamin A. Miller and Lauren H. Stephens and Nadya T. Bliss},
  journal={2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
Anomaly detection in graphs is a relevant problem in numerous applications. When determining whether an observation is anomalous with respect to the model of typical behavior, the notion of “goodness of fit” is important. This notion, however, is not well-understood in the context of graph data. In this paper, we propose three goodness-of-fit statistics for Chung-Lu random graphs, and analyze their efficacy in discriminating graphs generated by the Chung-Lu model from those with anomalous… CONTINUE READING