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/}
}