439 citations · 716 across the 8 of their papers we have counts for
9 papers
Generalised Implicit Neural Representations
Daniele Grattarola, Pierre Vandergheynst
We consider the problem of learning implicit neural representations (INRs) for signals on non-Euclidean domains. In the Euclidean case, INRs are trained on a discrete sampling of a…
Unsupervised Network Embedding Beyond Homophily
Zhiqiang Zhong, Guadalupe Gonzalez, Daniele Grattarola +1
Network embedding (NE) approaches have emerged as a predominant technique to represent complex networks and have benefited numerous tasks. However, most NE approaches rely on a hom…
Graph Neural Networks in TensorFlow and Keras with Spektral
Daniele Grattarola, Cesare Alippi
In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implem…
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…
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…