Exposing and Characterizing Subpopulations of Distinctly Regulated Genes by K-Plane Regression. - Fabrizio Frasca; Matteo Matteucci; Marco J. Morelli; Marco Masseroli (CIBB, 2018)

Modeling Gene Transcriptional Regulation by Means of Hyperplanes Genetic Clustering. - Fabrizio Frasca; Matteo Matteucci; Marco Masseroli; Marco J. Morelli (IJCNN, 2018)

Fake News Detection on Social Media using Geometric Deep Learning. - Federico Monti; Fabrizio Frasca; Davide Eynard; Damon Mannion; Michael M. Bronstein (CoRR, 2019)

Learning interpretable disease self-representations for drug repositioning. - Fabrizio Frasca; Diego Galeano; Guadalupe Gonzalez; Ivan Laponogov; Kirill A. Veselkov; Alberto Paccanaro; Michael M. Bronstein (CoRR, 2019)

SIGN: Scalable Inception Graph Neural Networks. - Emanuele Rossi; Fabrizio Frasca; Ben Chamberlain 001; Davide Eynard; Michael M. Bronstein; Federico Monti (CoRR, 2020)

Temporal Graph Networks for Deep Learning on Dynamic Graphs. - Emanuele Rossi; Ben Chamberlain 001; Fabrizio Frasca; Davide Eynard; Federico Monti; Michael M. Bronstein (CoRR, 2020)

Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. - Cristian Bodnar; Fabrizio Frasca; Yuguang Wang 001; Nina Otter; Guido F. Montúfar; Pietro Lió; Michael M. Bronstein (ICML, 2021)

Weisfeiler and Lehman Go Cellular: CW Networks. - Cristian Bodnar; Fabrizio Frasca; Nina Otter; Yuguang Wang 001; Pietro Liò; Guido F. Montúfar; Michael M. Bronstein (NeurIPS, 2021)

Equivariant Subgraph Aggregation Networks. - Beatrice Bevilacqua; Fabrizio Frasca; Derek Lim; Balasubramaniam Srinivasan; Chen Cai; Gopinath Balamurugan; Michael M. Bronstein; Haggai Maron (ICLR, 2022)

Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. - Fabrizio Frasca; Beatrice Bevilacqua; Michael M. Bronstein; Haggai Maron (NeurIPS, 2022)

Accurate and highly interpretable prediction of gene expression from histone modifications. - Fabrizio Frasca; Matteo Matteucci; Michele Leone; Marco J. Morelli; Marco Masseroli (BMC Bioinform., 2022)

Graph Neural Networks for Link Prediction with Subgraph Sketching. - Benjamin Paul Chamberlain; Sergey Shirobokov; Emanuele Rossi; Fabrizio Frasca; Thomas Markovich; Nils Yannick Hammerla; Michael M. Bronstein; Max Hansmire (ICLR, 2023)

Graph Positional Encoding via Random Feature Propagation. - Moshe Eliasof; Fabrizio Frasca; Beatrice Bevilacqua; Eran Treister; Gal Chechik; Haggai Maron (ICML, 2023)

Edge Directionality Improves Learning on Heterophilic Graphs. - Emanuele Rossi; Bertrand Charpentier; Francesco Di Giovanni; Fabrizio Frasca; Stephan Günnemann; Michael M. Bronstein (LoG, 2023)

Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting. - Giorgos Bouritsas; Fabrizio Frasca; Stefanos Zafeiriou; Michael M. Bronstein (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Position: Future Directions in the Theory of Graph Machine Learning. - Christopher Morris 001; Fabrizio Frasca; Nadav Dym; Haggai Maron; Ismail Ilkan Ceylan; Ron Levie; Derek Lim; Michael M. Bronstein; Martin Grohe; Stefanie Jegelka (ICML, 2024)

A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening. - Guy Bar-Shalom; Yam Eitan; Fabrizio Frasca; Haggai Maron (NeurIPS, 2024)

Future Directions in Foundations of Graph Machine Learning. - Christopher Morris 001; Nadav Dym; Haggai Maron; Ismail Ilkan Ceylan; Fabrizio Frasca; Ron Levie; Derek Lim; Michael M. Bronstein; Martin Grohe; Stefanie Jegelka (CoRR, 2024)

Topological Blind Spots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity. - Yam Eitan; Yoav Gelberg; Guy Bar-Shalom; Fabrizio Frasca; Michael M. Bronstein; Haggai Maron (CoRR, 2024)

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs. - Fabrizio Frasca; Fabian Jogl; Moshe Eliasof; Matan Ostrovsky; Carola-Bibiane Schönlieb; Thomas Gärtner 001; Haggai Maron (CoRR, 2024)

Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity. - Yam Eitan; Yoav Gelberg; Guy Bar-Shalom; Fabrizio Frasca; Michael M. Bronstein; Haggai Maron (ICLR, 2025)

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality. - Joshua Southern; Yam Eitan; Guy Bar-Shalom; Michael M. Bronstein; Haggai Maron; Fabrizio Frasca (CoRR, 2025)

Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks. - Maya Bechler-Speicher; Ben Finkelshtein; Fabrizio Frasca; Luis Müller; Jan Tönshoff; Antoine Siraudin; Viktor Zaverkin; Michael M. Bronstein; Mathias Niepert; Bryan Perozzi; Mikhail Galkin 001; Christopher Morris 001 (CoRR, 2025)

Learning on LLM Output Signatures for gray-box LLM Behavior Analysis. - Guy Bar-Shalom; Fabrizio Frasca; Derek Lim; Yoav Gelberg; Yftah Ziser; Ran El-Yaniv; Gal Chechik; Haggai Maron (CoRR, 2025)

Understanding and Improving Laplacian Positional Encodings For Temporal GNNs. - Yaniv Galron; Fabrizio Frasca; Haggai Maron; Eran Treister; Moshe Eliasof (CoRR, 2025)