Hierarchical representation learning in graph neural networks with node decimation pooling

FM Bianchi, D Grattarola, L Livi… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
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 …
被引用次数：67 相关文章

Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling

FM Bianchi, D Grattarola, L Livi, C Alippi - arXiv e-prints, 2019 - ui.adsabs.harvard.edu
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 …
[PDF] researchgate.net

[PDF][PDF] Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling

FM Bianchi, D Grattarola, L Livi, C Alippi - researchgate.net
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 …

Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling

FM Bianchi, D Grattarola, L Livi, C Alippi - 2020 - norceresearch.brage.unit.no
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 …
[PDF] academia.edu

[PDF][PDF] Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling

FM Bianchi, D Grattarola, L Livi, C Alippi - academia.edu
In graph neural networks (GNNs), pooling operators compute local summaries of input
graphs to capture their global properties; in turn, they are fundamental operators for building …

Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling

FM Bianchi, D Grattarola, L Livi, C Alippi - 2020 - munin.uit.no
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 …

Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling

FM Bianchi, D Grattarola, L Livi, C Alippi - arXiv preprint arXiv:1910.11436, 2019 - arxiv.org
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 …

Hierarchical Representation Learning in Graph Neural Networks With Node Decimation Pooling

FM Bianchi, D Grattarola, L Livi… - IEEE transactions on …, 2022 - pubmed.ncbi.nlm.nih.gov
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 …

Hierarchical Representation Learning in Graph Neural Networks With Node Decimation Pooling

FM Bianchi, D Grattarola, L Livi… - IEEE TRANSACTIONS …, 2021 - re.public.polimi.it
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 …

Hierarchical Representation Learning in Graph Neural Networks With Node Decimation Pooling.

FM Bianchi, D Grattarola, L Livi… - IEEE Transactions on …, 2022 - europepmc.org
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 …