268 citations · 1.1k across the 58 of their papers we have counts for
8 papers · 1 filter
Learning Stable Graph Neural Networks via Spectral Regularization
Zhan Gao, Elvin Isufi
Stability of graph neural networks (GNNs) characterizes how GNNs react to graph perturbations and provides guarantees for architecture performance in noisy scenarios. This paper de…
Graph Filters for Signal Processing and Machine Learning on Graphs
Elvin Isufi, Fernando Gama, David I. Shuman +1
Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and mac…
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis
Mohammad Sabbaqi, Elvin Isufi
Devising and analyzing learning models for spatiotemporal network data is of importance for tasks including forecasting, anomaly detection, and multi-agent coordination, among othe…
Graph filtering over expanding graphs
Bishwadeep Das, Elvin Isufi
Our capacity to learn representations from data is related to our ability to design filters that can leverage their coupling with the underlying domain. Graph filters are one such…
Learning Expanding Graphs for Signal Interpolation
Bishwadeep Das, Elvin Isufi
Performing signal processing over graphs requires knowledge of the underlying fixed topology. However, graphs often grow in size with new nodes appearing over time, whose connectiv…
Learning Stochastic Graph Neural Networks with Constrained Variance
Zhan Gao, Elvin Isufi
Stochastic graph neural networks (SGNNs) are information processing architectures that learn representations from data over random graphs. SGNNs are trained with respect to the exp…