Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Grattarola, Daniele"'
Cellular automata (CAs) are computational models exhibiting rich dynamics emerging from the local interaction of cells arranged in a regular lattice. Graph CAs (GCAs) generalise standard CAs by allowing for arbitrary graphs rather than regular lattic
Externí odkaz:
http://arxiv.org/abs/2301.10497
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 signal over a regular lattice. Here, we assume that the continuous si
Externí odkaz:
http://arxiv.org/abs/2205.15674
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 homophily assumption to learn embeddings with the guidance of supervisory
Externí odkaz:
http://arxiv.org/abs/2203.10866
Cellular automata (CA) are a class of computational models that exhibit rich dynamics emerging from the local interaction of cells arranged in a regular lattice. In this work we focus on a generalised version of typical CA, called graph cellular auto
Externí odkaz:
http://arxiv.org/abs/2110.14237
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems (Volume: 35, Issue: 2, February 2024)
Inspired by the conventional pooling layers in convolutional neural networks, many recent works in the field of graph machine learning have introduced pooling operators to reduce the size of graphs. The great variety in the literature stems from the
Externí odkaz:
http://arxiv.org/abs/2110.05292
Autor:
Grattarola, Daniele, Alippi, Cesare
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 implements a large set of methods for deep learning on graphs, including mes
Externí odkaz:
http://arxiv.org/abs/2006.12138
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 learn hierarchical representations. In this work, we propose the Node
Externí odkaz:
http://arxiv.org/abs/1910.11436
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 operations that aggregate nodes belonging to the same cluster. However,
Externí odkaz:
http://arxiv.org/abs/1907.00481
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 general family of graphs, characterised by a variable topology, and att
Externí odkaz:
http://arxiv.org/abs/1903.07299
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 by the auto-regressive moving average (ARMA) filter that, compared to
Externí odkaz:
http://arxiv.org/abs/1901.01343