Network Global Testing by Counting Graphlets
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jul 2018.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 1.
5 papers.
- 2023On the Power of the Weisfeiler-Leman Test for Graph Motif Parameterscited by 6
- 2022Higher-order accurate two-sample network inference and network hashingcited by 1
- 2023Testing High-dimensional Multinomials with Applications to Text Analysiscited by 1
- 2022A generative neural network model for random dot product graphscited by 0
- 2024Selecting the Number of Communities for Weighted Degree-Corrected Stochastic Block Modelscited by 0
- 2024Selecting the Number of Communities for Weighted Degree-Corrected Stochastic Block Models8 Jun 2024
- 2023On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters29 Sep 2023
- 2023Testing High-dimensional Multinomials with Applications to Text Analysis3 Jan 2023
- 2022Higher-order accurate two-sample network inference and network hashing16 Aug 2022
- 2022A generative neural network model for random dot product graphs15 Apr 2022