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Found 9 papers, 4 papers with code
Stability of Graph Convolutional Neural Networks through the lens of small perturbation analysis
In this work, we study the problem of stability of Graph Convolutional Neural Networks (GCNs) under random small perturbations in the underlying graph topology, i. e. under a limited number of insertions or deletions of edges.
Learning Multi-Frequency Partial Correlation Graphs
Despite the large research effort devoted to learning dependencies between time series, the state of the art still faces a major limitation: existing methods learn partial correlations but fail to discriminate across distinct frequency bands.
Generalized Simplicial Attention Neural Networks
The aim of this work is to introduce Generalized Simplicial Attention Neural Networks (GSANs), i. e., novel neural architectures designed to process data defined on simplicial complexes using masked self-attentional layers.
Topological Signal Processing over Weighted Simplicial Complexes
Weighing the topological domain over which data can be represented and analysed is a key strategy in many signal processing and machine learning applications, enabling the extraction and exploitation of meaningful data features and their (higher order) relationships.
Cell Attention Networks
In this paper, we introduce Cell Attention Networks (CANs), a neural architecture operating on data defined over the vertices of a graph, representing the graph as the 1-skeleton of a cell complex introduced to capture higher order interactions.
Ranked #7 on
Graph Classification
on NCI109
Topological Signal Processing over Generalized Cell Complexes
To overcome this limit, in this paper we extend TSP to deal with signals defined over cell complexes and we also generalize the concept of cell complexes to include hollow cells.
Topological Signal Processing over Cell Complexes
The Topological Signal Processing (TSP) framework has been recently developed to analyze signals defined over simplicial complexes, i. e. topological spaces represented by finite sets of elements that are closed under inclusion of subsets [1].
Topological Signal Processing over Simplicial Complexes
The goal of this paper is to establish the fundamental tools to analyze signals defined over a topological space, i. e. a set of points along with a set of neighborhood relations.
Adaptive Least Mean Squares Estimation of Graph Signals
The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of signals defined over graphs.
