36 citations · 55 across the 13 of their papers we have counts for
13 papers
Robust Community Detection for Noisy Networks with Covariates: Application to Functional Brain Networks
Zeyu Hu, Frederick H. Xu, Suprateek Kundu +4
Community detection is fundamental to understanding the modular organization in functional brain networks, yet noise in neuroimaging-derived networks and auxiliary node-level covar…
Sharp small-deviation inequalities for sums of independent nonnegative random variables
Weibo Fu, Yanjun Han, Guanyang Wang +3
Let be independent nonnegative random variables with , and write . For , we prove that \[ \mathbb{P}\left(S<\mathbb{E} S+δ\…
Gaussian Graphical Models for Functional Connectivity Analysis: A Statistical Review with Applications to Alzheimer's Disease
Panpan Zhang, Shiying Xiao, W. Hudson Robb +3
Functional connectivity analysis is an important tool for characterizing interactions among brain regions, particularly in studies of neurodegenerative disorders such as Alzheimer'…
When Does the Silhouette Score Work? A Comprehensive Study in Network Clustering
Zongyue Teng, Jun Yan, Dandan Liu +1
Selecting the number of communities is a fundamental challenge in network clustering. The silhouette score offers an intuitive, model-free criterion that balances within-cluster co…
Covariate Connectivity Combined Clustering for Weighted Networks
Zeyu Hu, Wenrui Li, Jun Yan +1
Community detection is a central task in network analysis, with applications in social, biological, and technological systems. Traditional algorithms rely primarily on network topo…
CALF-SBM: A Covariate-Assisted Latent Factor Stochastic Block Model
Sydney Louit, Evan Clark, Alexander Gelbard +3
We propose a novel network generative model extended from the standard stochastic block model by concurrently utilizing observed node-level information and accounting for network-e…
A Strength and Sparsity Preserving Algorithm for Generating Weighted, Directed Networks with Predetermined Assortativity
Yelie Yuan, Jun Yan, Panpan Zhang
Degree-preserving rewiring is a widely used technique for generating unweighted networks with given assortativity, but for weighted networks, it is unclear how an analog would pres…
Generating General Preferential Attachment Networks with R Package wdnet
Yelie Yuan, Tiandong Wang, Jun Yan +1
Preferential attachment (PA) network models have a wide range of applications in various scientific disciplines. Efficient generation of large-scale PA networks helps uncover their…
An Efficient Algorithm for Generating Directed Networks with Predetermined Assortativity Measures
Tiandong Wang, Jun Yan, Yelie Yuan +1
Assortativity coefficients are important metrics to analyze both directed and undirected networks. In general, it is not guaranteed that the fitted model will always agree with the…
PageRank centrality and algorithms for weighted, directed networks with applications to World Input-Output Tables
Panpan Zhang, Tiandong Wang, Jun Yan
PageRank (PR) is a fundamental tool for assessing the relative importance of the nodes in a network. In this paper, we propose a measure, weighted PageRank (WPR), extended from the…
Regional and Sectoral Structures and Their Dynamics of Chinese Economy: A Network Perspective from Multi-Regional Input-Output Tables
Tao Wang, Shiying Xiao, Jun Yan +1
A multi-regional input-output table (MRIOT) containing the transactions among the region-sectors in an economy defines a weighted and directed network. Using network analysis tools…
Assortativity measures for weighted and directed networks
Yelie Yuan, Jun Yan, Panpan Zhang
Assortativity measures the tendency of a vertex in a network being connected by other vertexes with respect to some vertex-specific features. Classical assortativity coefficients a…
Clustering US States by Time Series of COVID-19 New Case Counts with Non-negative Matrix Factorization
Jianmin Chen, Jun Yan, Panpan Zhang
The spreading pattern of COVID-19 differ a lot across the US states under different quarantine measures and reopening policies. We proposed to cluster the US states into distinct c…