# 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} }

- Published in IEEE International Conference on Data Mining…2018
DOI:10.1109/icdm.2018.00121

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

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