Multivariate time-series forecasting plays a crucial role in many real-world applications. It is
a challenging problem as one needs to consider both intra-series temporal correlations and …

Graphs are a central tool in machine learning and information processing as they allow to
conveniently capture the structure of complex datasets. In this context, it is of high …

Can one reduce the size of a graph without significantly altering its basic properties? The
graph reduction problem is hereby approached from the perspective of restricted spectral …

An emerging way to deal with high-dimensional noneuclidean data is to assume that the
underlying structure can be captured by a graph. Recently, ideas have begun to emerge …

Signal processing on graphs is an emerging research field dealing with signals living on an
irregular domain that is captured by a graph, and has been applied to sensor networks …

Graph Signal Processing (GSP) is an emerging research field that extends the concepts of
digital signal processing to graphs. GSP has numerous applications in different areas such …

Graph-based techniques emerged as a choice to deal with the dimensionality issues in
modeling multivariate time series. However, there is yet no complete understanding of how …

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 …

Graph signal processing deals with signals whose domain, defined by a graph, is irregular.
An overview of basic graph forms and definitions is presented first. Spectral analysis of …

We consider the problem of inferring the hidden structure of high-dimensional time-varying
data. In particular, we aim at capturing the dynamic relationships by representing data as …