Identifying graph topologies as well as processes evolving over graphs emerge in various
applications involving gene-regulatory, brain, power, and social networks, to name a few …

Network topology inference is a significant problem in network science. Most graph signal
processing (GSP) efforts to date assume that the underlying network is known and then …

Directed networks are pervasive both in nature and engineered systems, often underlying
the complex behavior observed in biological systems, microblogs and social interactions …

We address the problem of identifying the structure of an undirected graph from the
observation of signals defined on its nodes. Fundamentally, the unknown graph encodes …

Learning a graph topology to reveal the underlying relationship between data entities plays
an important role in various machine learning and data analysis tasks. Under the …

A task of major practical importance in network science is inferring the graph structure from
noisy observations at a subset of nodes. Available methods for topology inference typically …

Structural equation models (SEMs) have been widely adopted for inference of causal
interactions in complex networks. Recent examples include unveiling topologies of hidden …

Networks or graphs provide a natural and generic way for modeling rich structured data.
Recent research on graph analysis has been focused on representation learning, of which …

Modern data analytics applications on graphs often operate on domains where graph
topology is not known a priori, and hence its determination becomes part of the problem …

The promise of big data is enormous and nowhere is it more critical than in its potential to
contain important, undiscovered interdependence among thousands of variables. Networks …