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Reconstruction of time-varying graph signals via Sobolev smoothness
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 …
digital signal processing to graphs. GSP has numerous applications in different areas such …
Low-complexity graph sampling with noise and signal reconstruction via Neumann series
Graph sampling addresses the problem of selecting a node subset in a graph to collect
samples, so that a K-bandlimited signal can be reconstructed with high fidelity. Assuming an …
samples, so that a K-bandlimited signal can be reconstructed with high fidelity. Assuming an …
Sparse sampling for inverse problems with tensors
We consider the problem of designing sparse sampling strategies for multidomain signals,
which can be represented using tensors that admit a known multilinear decomposition. We …
which can be represented using tensors that admit a known multilinear decomposition. We …
Joint time-vertex linear canonical transform
The emergence of graph signal processing (GSP) has spurred a deep interest in signals that
naturally reside on irregularly structured data kernels, such as those found in social …
naturally reside on irregularly structured data kernels, such as those found in social …
Efficient node selection strategy for sampling bandlimited signals on graphs
This paper addresses the problem of selecting an optimal sampling set for-bandlimited
signals on graphs. The proposed sampling method is based on two proposed concepts of …
signals on graphs. The proposed sampling method is based on two proposed concepts of …
Learning graph ARMA processes from time-vertex spectra
The modeling of time-varying graph signals as stationary time-vertex stochastic processes
permits the inference of missing signal values by efficiently employing the correlation …
permits the inference of missing signal values by efficiently employing the correlation …
Graph-time convolutional neural networks
Spatiotemporal data can be represented as a process over a graph, which captures their
spatial relationships either explicitly or implicitly. How to leverage such a structure for …
spatial relationships either explicitly or implicitly. How to leverage such a structure for …
EEG as signal on graph: a multilayer network model for BCI applications
EEG signals acquired at different electrodes can be modelled as Signals on Graph, where
the graph structure reflects the underlying brain Functional Connectivity (FC), representing …
the graph structure reflects the underlying brain Functional Connectivity (FC), representing …
A joint Markov model for communities, connectivity and signals defined over graphs
Real-world networks are typically described in terms of nodes, links, and communities,
having signal values often associated with them. The aim of this letter is to introduce a novel …
having signal values often associated with them. The aim of this letter is to introduce a novel …
Sampling theory of jointly bandlimited time-vertex graph signals
Time-vertex graph signal (TVGS) models describe time-varying data with irregular
structures. The bandlimitedness in the joint time-vertex Fourier spectral domain reflects …
structures. The bandlimitedness in the joint time-vertex Fourier spectral domain reflects …