no code implementations • 10 Aug 2024 • Yam Eitan, Yoav Gelberg, Guy Bar-Shalom, Fabrizio Frasca, Michael Bronstein, Haggai Maron

Given the significant expressivity limitations of MPNNs, our paper aims to explore both the strengths and weaknesses of HOMP's expressive power and subsequently design novel architectures to address these limitations.

no code implementations • 13 Jun 2024 • Guy Bar-Shalom, Yam Eitan, Fabrizio Frasca, Haggai Maron

The product between the coarsened and the original graph reveals an implicit structure whereby subgraphs are associated with specific sets of nodes.

no code implementations • 3 Feb 2024 • Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, İsmail İlkan Ceylan, Ron Levie, Derek Lim, Michael Bronstein, Martin Grohe, Stefanie Jegelka

Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of disciplines, from life to social and engineering sciences.

1 code implementation • 17 May 2023 • Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael Bronstein

Graph Neural Networks (GNNs) have become the de-facto standard tool for modeling relational data.

Ranked #1 on Node Classification on Non-Homophilic (Heterophilic) Graphs on Chameleon (48%/32%/20% fixed splits)

Graph Neural Network Node Classification on Non-Homophilic (Heterophilic) Graphs

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.

1 code implementation • 30 Sep 2022 • Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Hammerla, Michael M. Bronstein, Max Hansmire

Our experiments show that BUDDY also outperforms SGNNs on standard LP benchmarks while being highly scalable and faster than ELPH.

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.

2 code implementations • NeurIPS 2021 • Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido Montúfar, Michael Bronstein

Nevertheless, these models can be severely constrained by the rigid combinatorial structure of Simplicial Complexes (SCs).

Ranked #1 on Graph Regression on ZINC 100k

2 code implementations • ICLR Workshop GTRL 2021 • Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montúfar, Pietro Liò, Michael Bronstein

The pairwise interaction paradigm of graph machine learning has predominantly governed the modelling of relational systems.

10 code implementations • 18 Jun 2020 • Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael Bronstein

Graph Neural Networks (GNNs) have recently become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of problems ranging from biology and particle physics to social networks and recommendation systems.

2 code implementations • 16 Jun 2020 • Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, Michael M. Bronstein

It has been shown that the expressive power of standard GNNs is bounded by the Weisfeiler-Leman (WL) graph isomorphism test, from which they inherit proven limitations such as the inability to detect and count graph substructures.

Ranked #2 on Graph Regression on ZINC 100k

5 code implementations • 23 Apr 2020 • Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, Federico Monti

Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media.

Ranked #5 on Node Classification on AMZ Comp

1 code implementation • 14 Sep 2019 • Fabrizio Frasca, Diego Galeano, Guadalupe Gonzalez, Ivan Laponogov, Kirill Veselkov, Alberto Paccanaro, Michael M. Bronstein

Here, we propose an interpretable model that learns disease self-representations for drug repositioning.

4 code implementations • 10 Feb 2019 • Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, Michael M. Bronstein

One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing.

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