928 citations · 1.6k across the 20 of their papers we have counts for
20 papers
Physics-Informed Topological Signal Processing for Water Distribution Network Monitoring
Tiziana Cattai, Stefania Sardellitti, Stefania Colonnese +2
Water management is one of the most critical aspects of our society, together with population increase and climate change. Water scarcity requires a better characterization and mon…
Robust Filter Design for Graph Signals
Lucia Testa, Stefania Sardellitti, Sergio Barbarossa
Our goal in this paper is the robust design of filters acting on signals observed over graphs subject to small perturbations of their edges. The focus is on developing a method to…
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…
Semantic Communications Based on Adaptive Generative Models and Information Bottleneck
S. Barbarossa, D. Comminiello, E. Grassucci +3
Semantic communications represent a significant breakthrough with respect to the current communication paradigm, as they focus on recovering the meaning behind the transmitted sequ…
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…
Topological Signal Processing over Weighted Simplicial Complexes
Claudio Battiloro, Stefania Sardellitti, Sergio Barbarossa +1
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 extracti…
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…
Topological Signal Processing over Generalized Cell Complexes
Stefania Sardellitti, Sergio Barbarossa
Topological Signal Processing (TSP) over simplicial complexes is a framework that has been recently proposed, as a generalization of graph signal processing (GSP), to extend GSP to…
Topological Signal Processing over Cell Complexes
Stefania Sardellitti, Sergio Barbarossa, Lucia Testa
The Topological Signal Processing (TSP) framework has been recently developed to analyze signals defined over simplicial complexes, i.e. topological spaces represented by finite se…
Topological Signal Processing over Simplicial Complexes
Sergio Barbarossa, Stefania Sardellitti
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. T…
Graph topology inference based on sparsifying transform learning
Stefania Sardellitti, Sergio Barbarossa, Paolo Di Lorenzo
Graph-based representations play a key role in machine learning. The fundamental step in these representations is the association of a graph structure to a dataset. In this paper,…
The edge cloud: A holistic view of communication, computation and caching
Sergio Barbarossa, Stefania Sardellitti, Elena Ceci +1
The evolution of communication networks shows a clear shift of focus from just improving the communications aspects to enabling new important services, from Industry 4.0 to automat…
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…
On the Graph Fourier Transform for Directed Graphs
Stefania Sardellitti, Sergio Barbarossa, Paolo Di Lorenzo
The analysis of signals defined over a graph is relevant in many applications, such as social and economic networks, big data or biological networks, and so on. A key tool for anal…
Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing
Stefania Sardellitti, Gesualdo Scutari, Sergio Barbarossa
Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices whil…
Distributed Detection and Estimation in Wireless Sensor Networks
Sergio Barbarossa, Stefania Sardellitti, Paolo Di Lorenzo
In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an…
Joint Optimization of Radio Resources and Code Partitioning in Mobile Edge Computing
Paolo Di Lorenzo, Sergio Barbarossa, Stefania Sardellitti
The aim of this paper is to propose a computation offloading strategy for mobile edge computing. We exploit the concept of call graph, which models a generic computer program as a…