Beatrice Bevilacqua
Ph.D. Candidate, Purdue University
bbevilac [AT] purdue.edu
Bio
Hi! I am a Ph.D. Candidate in Computer Science at Purdue University advised by Prof. Bruno Ribeiro. and also working closely with Prof. Haggai Maron.
My research interests lie in foundation models for structured data, invariances, reasoning.
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.
Holographic Node Representations: Pre-training Task-Agnostic Node Embeddings
Beatrice Bevilacqua, Joshua Robinson, Jure Leskovec, Bruno Ribeiro
ICLR 2025
Granola: Adaptive Normalization for Graph Neural Networks
Moshe Eliasof‡, Beatrice Bevilacqua‡, Carola-Bibiane Schönlieb, Haggai Maron
NeurIPS 2024
Efficient Subgraph GNNs by Learning Effective Selection Policies
Beatrice Bevilacqua, Moshe Eliasof, Eli Meirom, Bruno Ribeiro, Haggai Maron
ICLR 2024
Neural Algorithmic Reasoning with Causal Regularisation
Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Veličković
ICML 2023
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
Fabrizio Frasca‡, Beatrice Bevilacqua‡, Michael M. Bronstein, Haggai Maron
NeurIPS 2022 (Oral)
Equivariant Subgraph Aggregation Networks
Beatrice Bevilacqua‡, Fabrizio Frasca‡, Derek Lim‡, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron
ICLR 2022 (Spotlight)
Size-Invariant Graph Representations for Graph Classification Extrapolations
Beatrice Bevilacqua‡, Yangze Zhou‡, Bruno Ribeiro
ICML 2021 (Long Talk)
Holographic Node Representations: Pre-training Task-Agnostic Node Embeddings
Beatrice Bevilacqua, Joshua Robinson, Jure Leskovec, Bruno Ribeiro
ICLR 2025
TRIX: A More Expressive Model for Zero-shot Domain Transfer in Knowledge Graphs
Yucheng Zhang, Beatrice Bevilacqua, Mikhail Galkin, Bruno Ribeiro
LoG 2024
Digraf: Diffeomorphic Graph-Adaptive Activation Function
Krishna Sri Ipsit Mantri, Xinzhi Wang, Carola-Bibiane Schönlieb, Bruno Ribeiro, Beatrice Bevilacqua‡, Moshe Eliasof‡
NeurIPS 2024
Granola: Adaptive Normalization for Graph Neural Networks
Moshe Eliasof‡, Beatrice Bevilacqua‡, Carola-Bibiane Schönlieb, Haggai Maron
NeurIPS 2024
Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products
Guy Bar-Shalom, Beatrice Bevilacqua, Haggai Maron
ICML 2024
Long-Range Synthetic Knowledge Graph Benchmarks for Double-Equivariant Models
Bruna Jasinowodolinski, Yucheng Zhang, Jincheng Zhou, Beatrice Bevilacqua, Bruno Ribeiro
ICLR BGPT 2024
Efficient Subgraph GNNs by Learning Effective Selection Policies
Beatrice Bevilacqua, Moshe Eliasof, Eli Meirom, Bruno Ribeiro, Haggai Maron
ICLR 2024
A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes
Jincheng Zhou, Beatrice Bevilacqua, Bruno Ribeiro
NeurIPS GLFrontiers 2023
Subgraphormer: Subgraph GNNs meet Graph Transformers
Guy Bar-Shalom, Beatrice Bevilacqua, Haggai Maron
NeurIPS GLFrontiers 2023
Neural Algorithmic Reasoning with Causal Regularisation
Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Veličković
ICML 2023
Graph Positional Encoding via Random Feature Propagation
Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron
ICML 2023
Causal Lifting and Link Prediction
Leonardo Cotta, Beatrice Bevilacqua, Nesreen Ahmed, Bruno Ribeiro
The Royal Society A
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)
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
Fabrizio Frasca‡, Beatrice Bevilacqua‡, Michael M. Bronstein, Haggai Maron
NeurIPS 2022 (Oral)
Equivariant Subgraph Aggregation Networks
Beatrice Bevilacqua‡, Fabrizio Frasca‡, Derek Lim‡, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron
ICLR 2022 (Spotlight)
Size-Invariant Graph Representations for Graph Classification Extrapolations
Beatrice Bevilacqua‡, Yangze Zhou‡, Bruno Ribeiro
ICML 2021 (Long Talk)
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
- Instructor of Set and Graph Representations Classes within CS58700 ``Foundations of Deep Learning'' (Spring 2022 & Spring 2023), held by Prof. Bruno Ribeiro
- Reviewer ICLR, ICML 2022, 2023, 2024
- Top Reviewer NeurIPS 2022, 2023
- Reviewer TPAMI 2022
Experience
-
DeepMind Fall 2022Research Scientist, Intern
Neural Algorithmic Reasoning -
Meta AI (FAIR) Summer 2022Research Scientist, Intern
Computer Vision/Causal Models for OOD -
Purdue University 2020 - nowPh.D. Student
Computer Science -
Sapienza University Jan 2020 - Aug 2020Researcher
Interpretability of GNNs -
Purdue University Aug 2019 - Oct 2019Visiting Scholar
Expressive GNNs. Interview -
PiSchool of AI Apr 2019 - Jun 2019ML Scientist
Fault prediction