Network Global Testing by Counting Graphlets

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.

Cite

Text

Jin et al. "Network Global Testing by Counting Graphlets." International Conference on Machine Learning, 2018.

Markdown

[Jin et al. "Network Global Testing by Counting Graphlets." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/jin2018icml-network/)

BibTeX

@inproceedings{jin2018icml-network,
  title     = {{Network Global Testing by Counting Graphlets}},
  author    = {Jin, Jiashun and Ke, Zheng and Luo, Shengming},
  booktitle = {International Conference on Machine Learning},
  year      = {2018},
  pages     = {2333-2341},
  volume    = {80},
  url       = {https://mlanthology.org/icml/2018/jin2018icml-network/}
}