Simplicial complexes prove effective in modeling data with multiway
depe...
We propose a simplicial complex convolutional neural network (SCCNN) to ...
Data processing tasks over graphs couple the data residing over the node...
Implementing accurate Distribution System State Estimation (DSSE) faces
...
Filters are fundamental in extracting information from data. For time se...
Stability of graph neural networks (GNNs) characterizes how GNNs react t...
Devising and analyzing learning models for spatiotemporal network data i...
Our capacity to learn representations from data is related to our abilit...
Performing signal processing over graphs requires knowledge of the under...
Stochastic graph neural networks (SGNNs) are information processing
arch...
This paper proposes convolutional filtering for data whose structure can...
We study linear filters for processing signals supported on abstract
top...
This work proposes an algorithmic framework to learn time-varying graphs...
Graphs can model networked data by representing them as nodes and their
...
Graph convolutional neural networks (GCNNs) are nonlinear processing too...
In this paper, we study linear filters to process signals defined on
sim...
Spatiotemporal data can be represented as a process over a graph, which
...
Graph convolutional neural networks (GCNNs) learn compositional
represen...
Signal processing and machine learning algorithms for data supported ove...
Graph neural networks (GNNs) model nonlinear representations in graph da...
The forecasting of multi-variate time processes through graph-based
tech...
Distributed graph filters have found applications in wireless sensor net...
Network data can be conveniently modeled as a graph signal, where data v...
Driven by the outstanding performance of neural networks in the structur...
This paper reviews graph convolutional neural networks (GCNNs) through t...
This paper focuses on spectral filters on graphs, namely filters defined...
One of the cornerstones of the field of signal processing on graphs are ...