Search Results for author:
Found 12 papers, 2 papers with code
Gotta match 'em all: Solution diversification in graph matching matched filters
We present a novel approach for finding multiple noisily embedded template graphs in a very large background graph.
Bias-Variance Tradeoffs in Joint Spectral Embeddings
Joint spectral embeddings facilitate analysis of multiple network data by simultaneously mapping vertices in each network to points in Euclidean space where statistical inference is then performed.
Statistics Theory Statistics Theory 62H12, 62H15, 05C80
Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks
Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagreements.
Matched Filters for Noisy Induced Subgraph Detection
To illustrate the possibilities and challenges of such problems, we use an algorithm that can exploit a partially known correspondence and show via varied simulations and applications to {\it Drosophila} and human connectomes that this approach can achieve good performance.
Statistical inference on random dot product graphs: a survey
In this survey paper, we describe a comprehensive paradigm for statistical inference on random dot product graphs, a paradigm centered on spectral embeddings of adjacency and Laplacian matrices.
Connectome Smoothing via Low-rank Approximations
In statistical connectomics, the quantitative study of brain networks, estimating the mean of a population of graphs based on a sample is a core problem.
Analyzing statistical and computational tradeoffs of estimation procedures
The recent explosion in the amount and dimensionality of data has exacerbated the need of trading off computational and statistical efficiency carefully, so that inference is both tractable and meaningful.
Empirical Bayes Estimation for the Stochastic Blockmodel
Inference for the stochastic blockmodel is currently of burgeoning interest in the statistical community, as well as in various application domains as diverse as social networks, citation networks, brain connectivity networks (connectomics), etc.
Spectral Clustering for Divide-and-Conquer Graph Matching
We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs.
A central limit theorem for scaled eigenvectors of random dot product graphs
We prove a central limit theorem for the components of the largest eigenvectors of the adjacency matrix of a finite-dimensional random dot product graph whose true latent positions are unknown.
Universally consistent vertex classification for latent positions graphs
In this work we show that, using the eigen-decomposition of the adjacency matrix, we can consistently estimate feature maps for latent position graphs with positive definite link function $\kappa$, provided that the latent positions are i. i. d.
Statistical inference on errorfully observed graphs
Thus we errorfully observe $G$ when we observe the graph $\widetilde{G} = (V,\widetilde{E})$ as the edges in $\widetilde{E}$ arise from the classifications of the "edge-features", and are expected to be errorful.
