Given a pair of graphs G_1 and G_2 and a vertex set of interest in G_1, the vertex
nomination (VN) problem seeks to find the corresponding vertices of interest in G_2 (if they …

Given a vertex of interest in a network G 1, the vertex nomination problem seeks to find the
corresponding vertex of interest (if it exists) in a second network G 2. A vertex nomination …

Given a network and a subset of interesting vertices whose identities are only partially
known, the vertex nomination problem seeks to rank the remaining vertices in such a way …

Together with the curse of dimensionality, nonlinear dependencies in large data sets persist
as major challenges in data mining tasks. A reliable way to accurately preserve nonlinear …

Learning to rank–producing a ranked list of items specific to a query and with respect to a set
of supervisory items–is a problem of general interest. The setting we consider is one in …

As graph data becomes more ubiquitous, the need for robust inferential graph algorithms to
operate in these complex data domains is crucial. In many cases of interest, inference is …

We present a convex cone program to infer the latent probability matrix of a random dot
product graph (RDPG). The optimization problem maximizes the Bernoulli maximum …

Two-sample network hypothesis testing is an important inference task with applications
across diverse fields such as medicine, neuroscience, and sociology. Many of these testing …

Graph-valued data arises in numerous diverse scientific fields ranging from sociology,
epidemiology and genomics to neuroscience and economics. For example, sociologists …

Given a vertex of interest in a network $ G_1 $, the vertex nomination problem seeks to find
the corresponding vertex of interest (if it exists) in a second network $ G_2 $. A vertex …