Multi-level Hypothesis Testing for Populations of Heterogeneous Networks

@article{Gomes2018MultilevelHT,
  title={Multi-level Hypothesis Testing for Populations of Heterogeneous Networks},
  author={Guilherme Gomes and Vinayak Rao and Jennifer Neville},
  journal={2018 IEEE International Conference on Data Mining (ICDM)},
  year={2018},
  pages={977-982}
}
We consider hypothesis testing and anomaly detection on datasets where each observation is a weighted network. Current approaches to hypothesis testing for weighted networks typically require thresholding the edge-weights, to transform the data to binary networks. This results in a loss of information, and outcomes are sensitive to choice of threshold levels. Our work avoids this, and we consider weighted-graph observations in two situations, 1) where each graph belongs to one of two… CONTINUE READING
4
Twitter Mentions

References

Publications referenced by this paper.
SHOWING 1-10 OF 29 REFERENCES

Multi-level hypothesis testing for populations of heterogeneous networks

G. Gomes, V. Rao, J. Neville
  • Data Mining (ICDM), 2018 IEEE 18th International Conference on Data Mining. IEEE, 2018.
  • 2018

ngram: Fast n-gram tokenization

D. Schmidt, C. Heckendorf
  • 2017, R package version 3.0.4. [Online]. Available: https://cran.r-project.org/package=ngram
  • 2017
VIEW 1 EXCERPT

Inferring the mesoscale structure of layered, edge-valued, and time-varying networks.

  • Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2015
VIEW 1 EXCERPT

Similar Papers

Loading similar papers…