| Temporal graph networks for deep learning on dynamic graphs E Rossi, B Chamberlain, F Frasca, D Eynard, F Monti, M Bronstein arXiv preprint arXiv:2006.10637, 2020 | 874 | 2020 |
| Fake news detection on social media using geometric deep learning F Monti, F Frasca, D Eynard, D Mannion, MM Bronstein arXiv preprint arXiv:1902.06673, 2019 | 735 | 2019 |
| Improving graph neural network expressivity via subgraph isomorphism counting G Bouritsas, F Frasca, S Zafeiriou, MM Bronstein IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (1), 657-668, 2022 | 536 | 2022 |
| Sign: Scalable inception graph neural networks F Frasca, E Rossi, B Chamberlain, D Eynard, M Bronstein, F Monti arXiv preprint arXiv:2004.11198, 2020 | 460 | 2020 |
| Weisfeiler and lehman go cellular: Cw networks C Bodnar, F Frasca, N Otter, Y Wang, P Lio, GF Montufar, M Bronstein Advances in neural information processing systems 34, 2625-2640, 2021 | 326 | 2021 |
| Weisfeiler and lehman go topological: Message passing simplicial networks C Bodnar, F Frasca, Y Wang, N Otter, GF Montufar, P Lio, M Bronstein International conference on machine learning, 1026-1037, 2021 | 317 | 2021 |
| Equivariant subgraph aggregation networks B Bevilacqua, F Frasca, D Lim, B Srinivasan, C Cai, G Balamurugan, ... arXiv preprint arXiv:2110.02910, 2021 | 226 | 2021 |
| Understanding and extending subgraph gnns by rethinking their symmetries F Frasca, B Bevilacqua, M Bronstein, H Maron Advances in Neural Information Processing Systems 35, 31376-31390, 2022 | 158 | 2022 |
| Graph neural networks for link prediction with subgraph sketching BP Chamberlain, S Shirobokov, E Rossi, F Frasca, T Markovich, ... arXiv preprint arXiv:2209.15486, 2022 | 127 | 2022 |
| Edge directionality improves learning on heterophilic graphs E Rossi, B Charpentier, F Di Giovanni, F Frasca, S Günnemann, ... Learning on graphs conference, 25: 1-25: 27, 2024 | 82 | 2024 |
| Graph positional encoding via random feature propagation M Eliasof, F Frasca, B Bevilacqua, E Treister, G Chechik, H Maron International Conference on Machine Learning, 9202-9223, 2023 | 16 | 2023 |
| Position: Future directions in the theory of graph machine learning C Morris, F Frasca, N Dym, H Maron, II Ceylan, R Levie, D Lim, ... Forty-first International Conference on Machine Learning, 2024 | 13 | 2024 |
| Accurate and highly interpretable prediction of gene expression from histone modifications F Frasca, M Matteucci, M Leone, MJ Morelli, M Masseroli BMC bioinformatics 23 (1), 151, 2022 | 8 | 2022 |
| Topological blind spots: Understanding and extending topological deep learning through the lens of expressivity Y Eitan, Y Gelberg, G Bar-Shalom, F Frasca, M Bronstein, H Maron arXiv preprint arXiv:2408.05486, 2024 | 5 | 2024 |
| Future directions in the theory of graph machine learning C Morris, F Frasca, N Dym, H Maron, İİ Ceylan, R Levie, D Lim, ... arXiv preprint arXiv:2402.02287, 2024 | 4 | 2024 |
| Learning interpretable disease self-representations for drug repositioning F Frasca, D Galeano, G Gonzalez, I Laponogov, K Veselkov, A Paccanaro, ... arXiv preprint arXiv:1909.06609, 2019 | 4 | 2019 |
| Modeling gene transcriptional regulation by means of hyperplanes genetic clustering F Frasca, M Matteucci, M Masseroli, M Morelli 2018 International joint conference on neural networks (IJCNN), 1-8, 2018 | 4 | 2018 |
| A flexible, equivariant framework for subgraph gnns via graph products and graph coarsening G Bar-Shalom, Y Eitan, F Frasca, H Maron arXiv preprint arXiv:2406.09291, 2024 | 3 | 2024 |
| Exposing and characterizing subpopulations of distinctly regulated genes by k-plane regression F Frasca, M Matteucci, MJ Morelli, M Masseroli International Meeting on Computational Intelligence Methods for …, 2018 | 1 | 2018 |
| Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks M Bechler-Speicher, B Finkelshtein, F Frasca, L Müller, J Tönshoff, ... arXiv preprint arXiv:2502.14546, 2025 | | 2025 |
