928 citations · 1.6k across the 20 of their papers we have counts for
7 papers · 1 filter
Stability of Graph Convolutional Neural Networks through the lens of small perturbation analysis
Lucia Testa, Claudio Battiloro, Stefania Sardellitti +1
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 limite…
Learning Multi-Frequency Partial Correlation Graphs
Gabriele D'Acunto, Paolo Di Lorenzo, Francesco Bonchi +2
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 correlat…
Generalized Simplicial Attention Neural Networks
Claudio Battiloro, Lucia Testa, Lorenzo Giusti +3
Graph machine learning methods excel at leveraging pairwise relations present in the data. However, graphs are unable to fully capture the multi-way interactions inherent in many c…
Cell Attention Networks
Lorenzo Giusti, Claudio Battiloro, Lucia Testa +3
Since their introduction, graph attention networks achieved outstanding results in graph representation learning tasks. However, these networks consider only pairwise relationships…
Simplicial Attention Neural Networks
L. Giusti, C. Battiloro, P. Di Lorenzo +2
The aim of this work is to introduce simplicial attention networks (SANs), i.e., novel neural architectures that operate on data defined on simplicial complexes leveraging masked s…
Distributed Adaptive Learning of Graph Signals
P. Di Lorenzo, P. Banelli, S. Barbarossa +1
The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables dis…
Adaptive Least Mean Squares Estimation of Graph Signals
Paolo Di Lorenzo, Sergio Barbarossa, Paolo Banelli +1
The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of signals defined over graphs. Assuming the graph signal to be band-limited, over a…