Search Results for author:
Found 5 papers, 1 papers with code
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.
On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching
Given a graph in which a few vertices are deemed interesting a priori, the vertex nomination task is to order the remaining vertices into a nomination list such that there is a concentration of interesting vertices at the top of the list.
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.
On the Incommensurability Phenomenon
Suppose that two large, multi-dimensional data sets are each noisy measurements of the same underlying random process, and principle components analysis is performed separately on the data sets to reduce their dimensionality.
Seeded Graph Matching
Given two graphs, the graph matching problem is to align the two vertex sets so as to minimize the number of adjacency disagreements between the two graphs.
