We live life in the network. We check our e-mails regularly, make mobile phone calls from
almost any location, swipe transit cards to use public transportation, and make purchases …

The formalism of probabilistic graphical models provides a unifying framework for capturing
complex dependencies among random variables, and building large-scale multivariate …

Graph based semi-supervised learning (SSL) methods play an increasingly important role in
practical machine learning systems. A crucial step in graph based SSL methods is the …

Network alignment generalizes and unifies several approaches for forming a matching or
alignment between the vertices of two graphs. We study a mathematical programming …

Nonintrusive load monitoring refers to inferring what appliances are operating in a
household at a given time solely from fluctuations on the main power feeder. It is one …

Graph-based semi-supervised learning (SSL) methods play an increasingly important role in
practical machine learning systems, particularly in agnostic settings when no parametric …

Content sharing in social networks is a powerful mechanism for discovering content on the
Internet. The degree to which content is disseminated within the network depends on the …

There is a growing interest in building probabilistic models with high order potentials
(HOPs), or interactions, among discrete variables. Message passing inference in such …

Inference problems in graphical models can be represented as a constrained optimization of
a free-energy function. In this paper, we treat both forms of probabilistic inference, estimating …

The first step in graph-based semi-supervised classification is to construct a graph from input
data. While the k-nearest neighbor graphs have been the de facto standard method of graph …