Special invited paper: The SCORE normalization, especially for heterogeneous network and text data
SCORE was introduced as a spectral approach to network community detection. Since many
networks have severe degree heterogeneity, the ordinary spectral clustering (OSC) …
networks have severe degree heterogeneity, the ordinary spectral clustering (OSC) …
Optimal estimation of the number of network communities
In network analysis, how to estimate the number of communities K is a fundamental problem.
We consider a broad setting where we allow severe degree heterogeneity and a wide range …
We consider a broad setting where we allow severe degree heterogeneity and a wide range …
Fast network community detection with profile-pseudo likelihood methods
The stochastic block model is one of the most studied network models for community
detection, and fitting its likelihood function on large-scale networks is known to be …
detection, and fitting its likelihood function on large-scale networks is known to be …
On hyperparameter tuning in general clustering problemsm
Tuning hyperparameters for unsupervised learning problems is difficult in general due to the
lack of ground truth for validation. However, the success of most clustering methods …
lack of ground truth for validation. However, the success of most clustering methods …
Optimal adaptivity of signed-polygon statistics for network testing
Optimal adaptivity of signed-polygon statistics for network testing Page 1 The Annals of
Statistics 2021, Vol. 49, No. 6, 3408–3433 https://doi.org/10.1214/21-AOS2089 © Institute of …
Statistics 2021, Vol. 49, No. 6, 3408–3433 https://doi.org/10.1214/21-AOS2089 © Institute of …
Two-sample test of stochastic block models
In this paper, we consider the problem of two-sample test of large networks with community
structures. A test statistic is proposed based on the maximum entry of the difference between …
structures. A test statistic is proposed based on the maximum entry of the difference between …
GBTM: Community detection and network reconstruction for noisy and time-evolving data
X Chen, J Hu, Y Chen - Information Sciences, 2024 - Elsevier
Community detection and network reconstruction are two major concerns in network
analysis. However, these two tasks are extremely challenging since most of the existing …
analysis. However, these two tasks are extremely challenging since most of the existing …
Wilks' theorems in the -model
Likelihood ratio tests and the Wilks theorems have been pivotal in statistics but have rarely
been explored in network models with an increasing dimension. We are concerned here …
been explored in network models with an increasing dimension. We are concerned here …
Two-sample test for stochastic block models via maximum entry-wise deviation
K Fu, J Hu, S Keita, H Liu - arXiv preprint arXiv:2211.08668, 2022 - arxiv.org
The stochastic block model is a popular tool for detecting community structures in network
data. Detecting the difference between two community structures is an important issue for …
data. Detecting the difference between two community structures is an important issue for …
Profile‐pseudo likelihood methods for community detection of multilayer stochastic block models
K Fu, J Hu - Stat, 2023 - Wiley Online Library
The multilayer stochastic block model is one of the fundamental models in multilayer
networks and is often used to represent multiple types of relations between different …
networks and is often used to represent multiple types of relations between different …