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09/14/2023
Hodge-Aware Contrastive Learning
Simplicial complexes prove effective in modeling data with multiway depe...
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01/26/2023
Convolutional Learning on Simplicial Complexes
We propose a simplicial complex convolutional neural network (SCCNN) to ...
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01/17/2023
Online Filtering over Expanding Graphs
Data processing tasks over graphs couple the data residing over the node...
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01/04/2023
Deep Statistical Solver for Distribution System State Estimation
Implementing accurate Distribution System State Estimation (DSSE) faces ...
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11/16/2022
Graph Filters for Signal Processing and Machine Learning on Graphs
Filters are fundamental in extracting information from data. For time se...
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11/13/2022
Learning Stable Graph Neural Networks via Spectral Regularization
Stability of graph neural networks (GNNs) characterizes how GNNs react t...
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06/30/2022
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis
Devising and analyzing learning models for spatiotemporal network data i...
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03/15/2022
Graph filtering over expanding graphs
Our capacity to learn representations from data is related to our abilit...
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03/15/2022
Learning Expanding Graphs for Signal Interpolation
Performing signal processing over graphs requires knowledge of the under...
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01/29/2022
Learning Stochastic Graph Neural Networks with Constrained Variance
Stochastic graph neural networks (SGNNs) are information processing arch...
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01/29/2022
Convolutional Filtering in Simplicial Complexes
This paper proposes convolutional filtering for data whose structure can...
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01/27/2022
Simplicial Convolutional Filters
We study linear filters for processing signals supported on abstract top...
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10/21/2021
Learning Time-Varying Graphs from Online Data
This work proposes an algorithmic framework to learn time-varying graphs...
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10/06/2021
Simplicial Convolutional Neural Networks
Graphs can model networked data by representing them as nodes and their ...
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06/19/2021
Stability of Graph Convolutional Neural Networks to Stochastic Perturbations
Graph convolutional neural networks (GCNNs) are nonlinear processing too...
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03/23/2021
Finite Impulse Response Filters for Simplicial Complexes
In this paper, we study linear filters to process signals defined on sim...
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03/02/2021
Graph-Time Convolutional Neural Networks
Spatiotemporal data can be represented as a process over a graph, which ...
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10/27/2020
Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNNs) learn compositional represen...
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10/22/2020
Online Time-Varying Topology Identification via Prediction-Correction Algorithms
Signal processing and machine learning algorithms for data supported ove...
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06/04/2020
Stochastic Graph Neural Networks
Graph neural networks (GNNs) model nonlinear representations in graph da...
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04/17/2020
Forecasting Multi-Dimensional Processes over Graphs
The forecasting of multi-variate time processes through graph-based tech...
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04/14/2020
Quantization Analysis and Robust Design for Distributed Graph Filters
Distributed graph filters have found applications in wireless sensor net...
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03/08/2020
Graphs, Convolutions, and Neural Networks
Network data can be conveniently modeled as a graph signal, where data v...
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01/21/2020
EdgeNets:Edge Varying Graph Neural Networks
Driven by the outstanding performance of neural networks in the structur...
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03/04/2019
Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs
This paper reviews graph convolutional neural networks (GCNNs) through t...
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01/29/2019
On the Transferability of Spectral Graph Filters
This paper focuses on spectral filters on graphs, namely filters defined...
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02/14/2016
