32,416 research outputs found
Causal Fourier Analysis on Directed Acyclic Graphs and Posets
We present a novel form of Fourier analysis, and associated signal processing
concepts, for signals (or data) indexed by edge-weighted directed acyclic
graphs (DAGs). This means that our Fourier basis yields an eigendecomposition
of a suitable notion of shift and convolution operators that we define. DAGs
are the common model to capture causal relationships between data values and in
this case our proposed Fourier analysis relates data with its causes under a
linearity assumption that we define. The definition of the Fourier transform
requires the transitive closure of the weighted DAG for which several forms are
possible depending on the interpretation of the edge weights. Examples include
level of influence, distance, or pollution distribution. Our framework is
different from prior GSP: it is specific to DAGs and leverages, and extends,
the classical theory of Moebius inversion from combinatorics. For a
prototypical application we consider DAGs modeling dynamic networks in which
edges change over time. Specifically, we model the spread of an infection on
such a DAG obtained from real-world contact tracing data and learn the
infection signal from samples assuming sparsity in the Fourier domain.Comment: 13 pages, 11 figure
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
Tracking Time-Vertex Propagation using Dynamic Graph Wavelets
Graph Signal Processing generalizes classical signal processing to signal or
data indexed by the vertices of a weighted graph. So far, the research efforts
have been focused on static graph signals. However numerous applications
involve graph signals evolving in time, such as spreading or propagation of
waves on a network. The analysis of this type of data requires a new set of
methods that fully takes into account the time and graph dimensions. We propose
a novel class of wavelet frames named Dynamic Graph Wavelets, whose time-vertex
evolution follows a dynamic process. We demonstrate that this set of functions
can be combined with sparsity based approaches such as compressive sensing to
reveal information on the dynamic processes occurring on a graph. Experiments
on real seismological data show the efficiency of the technique, allowing to
estimate the epicenter of earthquake events recorded by a seismic network
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