L-2 Regularized maximum likelihood for -model in large and sparse networks
The $\beta $-model is a powerful tool for modeling network generation driven by degree
heterogeneity. Its simple yet expressive nature particularly well-suits large and sparse …
heterogeneity. Its simple yet expressive nature particularly well-suits large and sparse …
Affiliation weighted networks with a differentially private degree sequence
J Luo, T Liu, Q Wang - Statistical Papers, 2022 - Springer
Affiliation network is one kind of two-mode social network with two different sets of nodes
(namely, a set of actors and a set of social events) and edges representing the affiliation of …
(namely, a set of actors and a set of social events) and edges representing the affiliation of …
Asymptotic theory in network models with covariates and a growing number of node parameters
Q Wang, Y Zhang, T Yan - Annals of the Institute of Statistical Mathematics, 2023 - Springer
We propose a general model that jointly characterizes degree heterogeneity and homophily
in weighted, undirected networks. We present a moment estimation method using node …
in weighted, undirected networks. We present a moment estimation method using node …
Asymptotic in the Ordered Networks with a Noisy Degree Sequence
J Luo, H Qin - Journal of Systems Science and Complexity, 2022 - Springer
In the case of the differential privacy under the Laplace mechanism, the asymptotic
properties of parameter estimators have been derived in some special network models with …
properties of parameter estimators have been derived in some special network models with …
Parametric and Non-Parametric Methods for Statistical Network Inference
M Shao - 2023 - rave.ohiolink.edu
Network data, prevalent in domains like social networks, brain imaging, and transportation,
have attracted significant research interest from statisticians. This dissertation concentrates …
have attracted significant research interest from statisticians. This dissertation concentrates …
A note on asymptotic distributions in a directed network model with degree heterogeneity and homophily
J Luo, X Ma, L Zhou - Communications in Statistics-Theory and …, 2023 - Taylor & Francis
The asymptotic normality of a fixed number of the maximum likelihood estimators in a
directed network model with degree heterogeneity and homophily has been established …
directed network model with degree heterogeneity and homophily has been established …
A note on a dynamic network model with homogeneous structure
Y Long, T Huang - Statistics & Probability Letters, 2022 - Elsevier
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A unified framework for inference in network models with degree heterogeneity and homophily
T Yan - arXiv preprint arXiv:1806.02550, 2018 - arxiv.org
The degree heterogeneity and homophily are two typical features in network data. In this
paper, we formulate a general model for undirected networks with these two features and …
paper, we formulate a general model for undirected networks with these two features and …
A note on asymptotic distributions in a network model with degree heterogeneity and homophily
J Luo, H Qin, W Wang, J Wang - Communications in Statistics …, 2020 - Taylor & Francis
The asymptotic normality of a fixed number of the maximum likelihood estimators in a
network model with degree heterogeneity and homophily has been established recently. In …
network model with degree heterogeneity and homophily has been established recently. In …