136 citations · 408 across the 33 of their papers we have counts for
38 papers
Consistent Identification of Top- Nodes in Noisy Networks
Hui Shen, Eric D. Kolaczyk
Identifying the most influential nodes in a network, typically using centrality measures, is a central task in applied network analysis. However, real-world networks are often cons…
Autoregressive networks with dependent edges
Jinyuan Chang, Qin Fang, Eric D. Kolaczyk +2
We propose an autoregressive framework for modelling dynamic networks with dependent edges. It encompasses models that accommodate, for example, transitivity, degree heterogenenity…
Stochastic gradient descent-based inference for dynamic network models with attractors
Hancong Pan, Xiaojing Zhu, Cantay Caliskan +4
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 a…
Differentially Private Linear Regression with Linked Data
Shurong Lin, Elliot Paquette, Eric D. Kolaczyk
There has been increasing demand for establishing privacy-preserving methodologies for modern statistics and machine learning. Differential privacy, a mathematical notion from comp…
Differentially Private Confidence Intervals for Proportions under Stratified Random Sampling
Shurong Lin, Mark Bun, Marco Gaboardi +2
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…
Quantifying Uncertainty for Temporal Motif Estimation in Graph Streams under Sampling
Xiaojing Zhu, Eric D. Kolaczyk
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…