1 citations · 1 across the 9 of their papers we have counts for
9 papers
Estimating peer effects in noisy, low-rank networks via network smoothing
Alex Hayes, Keith Levin
Peer effect estimation requires precise network measurement, yet most empirical networks are noisy, rendering standard estimators inconsistent. To address measurement error in netw…
Adjacency Spectral Embeddings of Correlation Networks
Keith Levin
In many applications, weighted networks are constructed based on time series data: each time series is associated to a vertex and edge weights are given by pairwise correlations. T…
Matching and mixing: Matchability of graphs under Markovian error
Zhirui Li, Keith D. Levin, Zhiang Zhao +1
We consider the problem of graph matching for a sequence of graphs generated under a time-dependent Markov chain noise model. Our edgelighter error model, a variant of the classica…
On the Effect of Misspecifying the Embedding Dimension in Low-rank Network Models
Roddy Taing, Keith Levin
As network data has become ubiquitous in the sciences, there has been growing interest in network models whose structure is driven by latent node-level variables in a (typically lo…
Testing for correlation between network structure and high-dimensional node covariates
Alexander Fuchs-Kreiss, Keith Levin
In many application domains, networks are observed with node-level features. In such settings, a common problem is to assess whether or not nodal covariates are correlated with the…
Testing for Repeated Motifs and Hierarchical Structure in Stochastic Blockmodels
Al-Fahad Al-Qadhi, Keith Levin, Vincent Lyzinski
The rise in complexity of network data in neuroscience, social networks, and protein-protein interaction networks has been accompanied by several efforts to model and understand th…
Identifying rich clubs in spatiotemporal interaction networks
Jacob Kruse, Song Gao, Yuhan Ji +3
Spatial networks are widely used in various fields to represent and analyze interactions or relationships between locations or spatially distributed entities.There is a network sci…
Minimax rates for the linear-in-means model reveal an identifiability-estimability gap
Alex Hayes, Keith Levin
The linear-in-means model is widely used to study peer influence in social networks. We consider estimation in the linear-in-means model when a randomized treatment is applied to n…
Estimating network-mediated causal effects via principal components network regression
Alex Hayes, Mark M. Fredrickson, Keith Levin
We develop a method to decompose causal effects on a social network into an indirect effect mediated by the network, and a direct effect independent of the social network. To handl…