1 code implementation • 2 Jul 2024 • Krishna Sri Ipsit Mantri, Xinzhi Wang, Carola-Bibiane Schönlieb, Bruno Ribeiro, Beatrice Bevilacqua, Moshe Eliasof
In this paper, we propose a novel activation function tailored specifically for graph data in Graph Neural Networks (GNNs).
no code implementations • 20 Apr 2024 • Moshe Eliasof, Beatrice Bevilacqua, Carola-Bibiane Schönlieb, Haggai Maron
In recent years, significant efforts have been made to refine the design of Graph Neural Network (GNN) layers, aiming to overcome diverse challenges, such as limited expressive power and oversmoothing.
1 code implementation • 13 Feb 2024 • Guy Bar-Shalom, Beatrice Bevilacqua, Haggai Maron
In the realm of Graph Neural Networks (GNNs), two exciting research directions have recently emerged: Subgraph GNNs and Graph Transformers.
1 code implementation • 30 Oct 2023 • Beatrice Bevilacqua, Moshe Eliasof, Eli Meirom, Bruno Ribeiro, Haggai Maron
Subgraph GNNs are provably expressive neural architectures that learn graph representations from sets of subgraphs.
no code implementations • 12 Jul 2023 • Jincheng Zhou, Beatrice Bevilacqua, Bruno Ribeiro
The task of inductive link prediction in (discrete) attributed multigraphs infers missing attributed links (relations) between nodes in new test multigraphs.
no code implementations • 6 Mar 2023 • Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron
Two main families of node feature augmentation schemes have been explored for enhancing GNNs: random features and spectral positional encoding.
no code implementations • 20 Feb 2023 • Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Veličković
We evaluate our method on the CLRS algorithmic reasoning benchmark, where we show up to 3$\times$ improvements on the OOD test data.
no code implementations • 2 Feb 2023 • Leonardo Cotta, Beatrice Bevilacqua, Nesreen Ahmed, Bruno Ribeiro
Existing causal models for link prediction assume an underlying set of inherent node factors -- an innate characteristic defined at the node's birth -- that governs the causal evolution of links in the graph.
2 code implementations • 22 Sep 2022 • Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Dudzik, Matko Bošnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, Petar Veličković
The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution.
2 code implementations • 22 Jun 2022 • Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron
Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which model graphs as collections of subgraphs.
1 code implementation • ICLR 2022 • Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron
Thus, we propose to represent each graph as a set of subgraphs derived by some predefined policy, and to process it using a suitable equivariant architecture.
1 code implementation • 8 Mar 2021 • Beatrice Bevilacqua, Yangze Zhou, Bruno Ribeiro
In general, graph representation learning methods assume that the train and test data come from the same distribution.
no code implementations • 1 Jan 2021 • Beatrice Bevilacqua, Yangze Zhou, Ryan L Murphy, Bruno Ribeiro
Extrapolation in graph classification/regression remains an underexplored area of an otherwise rapidly developing field.