10 citations · 18 across the 12 of their papers we have counts for
5 papers · 1 filter
Simplicial Convolutional Neural Networks
Maosheng Yang, Elvin Isufi, Geert Leus
Graphs can model networked data by representing them as nodes and their pairwise relationships as edges. Recently, signal processing and neural networks have been extended to proce…
Stability of Graph Convolutional Neural Networks to Stochastic Perturbations
Zhan Gao, Elvin Isufi, Alejandro Ribeiro
Graph convolutional neural networks (GCNNs) are nonlinear processing tools to learn representations from network data. A key property of GCNNs is their stability to graph perturbat…
Graph-Time Convolutional Neural Networks
Elvin Isufi, Gabriele Mazzola
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 l…
Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs
Elvin Isufi, Fernando Gama, Alejandro Ribeiro
This paper reviews graph convolutional neural networks (GCNNs) through the lens of edge-variant graph filters. The edge-variant graph filter is a finite order, linear, and local re…
On the Transferability of Spectral Graph Filters
Ron Levie, Elvin Isufi, Gitta Kutyniok
This paper focuses on spectral filters on graphs, namely filters defined as elementwise multiplication in the frequency domain of a graph. In many graph signal processing settings,…