439 citations · 1.4k across the 66 of their papers we have counts for
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Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi +1
In graph neural networks (GNNs), pooling operators compute local summaries of input graphs to capture their global properties, and they are fundamental for building deep GNNs that…
Graph Random Neural Features for Distance-Preserving Graph Representations
Daniele Zambon, Cesare Alippi, Lorenzo Livi
We present Graph Random Neural Features (GRNF), a novel embedding method from graph-structured data to real vectors based on a family of graph neural networks. The embedding natura…
Distributed Deep Convolutional Neural Networks for the Internet-of-Things
Simone Disabato, Manuel Roveri, Cesare Alippi
Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically dema…
Deep Learning for Time Series Forecasting: The Electric Load Case
Alberto Gasparin, Slobodan Lukovic, Cesare Alippi
Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remain…
Spectral Clustering with Graph Neural Networks for Graph Pooling
Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi
Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph. SC can be used in Graph Neural Networks (GNNs) to implement pooling op…
Autoregressive Models for Sequences of Graphs
Daniele Zambon, Daniele Grattarola, Lorenzo Livi +1
This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models. A first novelty consists in formalising the AR model for a very g…
Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere
Pietro Verzelli, Cesare Alippi, Lorenzo Livi
Among the various architectures of Recurrent Neural Networks, Echo State Networks (ESNs) emerged due to their simplified and inexpensive training procedure. These networks are know…
Graph Neural Networks with convolutional ARMA filters
Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi +1
Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired…