Network Global Testing by Counting Graphlets

@InProceedings{pmlr-v80-jin18b,
  title = 	 {Network Global Testing by Counting Graphlets},
  author =       {Jin, Jiashun and Ke, Zheng and Luo, Shengming},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {2333--2341},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--15 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v80/jin18b/jin18b.pdf},
  url = 	 {https://proceedings.mlr.press/v80/jin18b.html},
  abstract = 	 {Consider a large social network with possibly severe degree heterogeneity and mixed-memberships. We are interested in testing whether the network has only one community or there are more than one communities. The problem is known to be non-trivial, partially due to the presence of severe degree heterogeneity. We construct a class of test statistics using the numbers of short paths and short cycles, and the key to our approach is a general framework for canceling the effects of degree heterogeneity. The tests compare favorably with existing methods. We support our methods with careful analysis and numerical study with simulated data and a real data example.}
}