Beatrice Bevilacqua

Ph.D. Student, Purdue University

bbevilac [AT] purdue.edu

Bio

Hi! I am a Ph.D. Student in Computer Science at Purdue University advised by Prof. Bruno Ribeiro.

During Summer 2021 I participated to the LogML summer school, which led to a great series of works with Dr. Haggai Maron on Subgraph Neural Networks.

My research interests are expressivity and extrapolation capabilities of Graph Neural Networks.

Previously (Fall 2022), I was a Research Scientist Intern at DeepMind working with Dr. Petar Veličković on Neural Algorithmic Reasoning.

Publications

Most recent publications on Google Scholar. ‡ indicates equal contribution.

Efficient Subgraph GNNs by Learning Effective Selection Policies

Beatrice Bevilacqua, Moshe Eliasof, Eli Meirom, Bruno Ribeiro, Haggai Maron

ICLR 2024

Paper

Neural Algorithmic Reasoning with Causal Regularisation

Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Veličković

ICML 2023

Paper

Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries

Fabrizio Frasca‡, Beatrice Bevilacqua‡, Michael M. Bronstein, Haggai Maron

NeurIPS 2022 (Oral)

Paper Code

Equivariant Subgraph Aggregation Networks

Beatrice Bevilacqua‡, Fabrizio Frasca‡, Derek Lim‡, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron

ICLR 2022 (Spotlight)

Paper Video Code

Size-Invariant Graph Representations for Graph Classification Extrapolations

Beatrice Bevilacqua‡, Yangze Zhou‡, Bruno Ribeiro

ICML 2021 (Long Talk)

Paper Code

Efficient Subgraph GNNs by Learning Effective Selection Policies

Beatrice Bevilacqua, Moshe Eliasof, Eli Meirom, Bruno Ribeiro, Haggai Maron

ICLR 2024

Paper

A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes

Jincheng Zhou, Beatrice Bevilacqua, Bruno Ribeiro

NeurIPS GLFrontiers 2023

Paper

Subgraphormer: Subgraph GNNs meet Graph Transformers

Guy Bar-Shalom, Beatrice Bevilacqua, Haggai Maron

NeurIPS GLFrontiers 2023

Paper

Neural Algorithmic Reasoning with Causal Regularisation

Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Veličković

ICML 2023

Paper

Graph Positional Encoding via Random Feature Propagation

Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron

ICML 2023

Paper

Causal Lifting and Link Prediction

Leonardo Cotta, Beatrice Bevilacqua, Nesreen Ahmed, Bruno Ribeiro

The Royal Society A

Paper

A Generalist Neural Algorithmic Learner

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

LoG (Spotlight)

Paper

Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries

Fabrizio Frasca‡, Beatrice Bevilacqua‡, Michael M. Bronstein, Haggai Maron

NeurIPS 2022 (Oral)

Paper Code

Equivariant Subgraph Aggregation Networks

Beatrice Bevilacqua‡, Fabrizio Frasca‡, Derek Lim‡, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron

ICLR 2022 (Spotlight)

Paper Video Code

Size-Invariant Graph Representations for Graph Classification Extrapolations

Beatrice Bevilacqua‡, Yangze Zhou‡, Bruno Ribeiro

ICML 2021 (Long Talk)

Paper Code

Simulation-Based Evolutionary Optimization of Air Traffic Management

Alessandro Pellegrini, Pierangelo di Sanzo, Beatrice Bevilacqua, Gabriella Duca, Domenico Pascarella, Roberto Palumbo, Juan Josè Ramos, Miquel Àngel Piera, Gabriella Gigante

IEEE Access 2020

Service

Experience

Beatrice Bevilacqua
Based on Martin Saveski's template.