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pro vyhledávání: 'Kolaczyk, Eric D.'
We propose an autoregressive framework for modelling dynamic networks with dependent edges. It encompasses the models which accommodate, for example, transitivity, density-dependent and other stylized features often observed in real network data. By
Externí odkaz:
http://arxiv.org/abs/2404.15654
Autor:
Pan, Hancong, Zhu, Xiaojing, Caliskan, Cantay, Christenson, Dino P., Spiliopoulos, Konstantinos, Walker, Dylan, Kolaczyk, Eric D.
In Coevolving Latent Space Networks with Attractors (CLSNA) models, nodes in a latent space represent social actors, and edges indicate their dynamic interactions. Attractors are added at the latent level to capture the notion of attractive and repul
Externí odkaz:
http://arxiv.org/abs/2403.07124
There has been increasing demand for establishing privacy-preserving methodologies for modern statistics and machine learning. Differential privacy, a mathematical notion from computer science, is a rising tool offering robust privacy guarantees. Rec
Externí odkaz:
http://arxiv.org/abs/2308.00836
Publikováno v:
Electronic Journal of Statistics, Electron. J. Statist. 18(1), 1455-1494, (2024)
Confidence intervals are a fundamental tool for quantifying the uncertainty of parameters of interest. With the increase of data privacy awareness, developing a private version of confidence intervals has gained growing attention from both statistici
Externí odkaz:
http://arxiv.org/abs/2301.08324
Autor:
Zhu, Xiaojing, Kolaczyk, Eric D.
Dynamic networks, a.k.a. graph streams, consist of a set of vertices and a collection of timestamped interaction events (i.e., temporal edges) between vertices. Temporal motifs are defined as classes of (small) isomorphic induced subgraphs on graph s
Externí odkaz:
http://arxiv.org/abs/2202.10513
Publikováno v:
Annals of Statistics 2024, Vol. 52, pp. 708-728
A standing challenge in data privacy is the trade-off between the level of privacy and the efficiency of statistical inference. Here we conduct an in-depth study of this trade-off for parameter estimation in the $\beta$-model (Chatterjee, Diaconis an
Externí odkaz:
http://arxiv.org/abs/2112.10151
Autor:
Zhu, Xiaojing, Caliskan, Cantay, Christenson, Dino P., Spiliopoulos, Konstantinos, Walker, Dylan, Kolaczyk, Eric D.
We develop a broadly applicable class of coevolving latent space network with attractors (CLSNA) models, where nodes represent individual social actors assumed to lie in an unknown latent space, edges represent the presence of a specified interaction
Externí odkaz:
http://arxiv.org/abs/2109.13129