Fabrizio Frasca

44 papers A* 11B 1Journal 28Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Beyond Next Token Probabilities: Learnable, Fast Detection of Hallucinations and Data Contamination on LLM Output Distributions. AAAI Guy Bar-Shalom, Fabrizio Frasca, Derek Lim, Yoav Gelberg, Yftah Ziser, Ran El-Yaniv, Gal Chechik, Haggai Maron
2025	A*	`conf`	Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality. ICML Joshua Southern, Yam Eitan, Guy Bar-Shalom, Michael M. Bronstein, Haggai Maron, Fabrizio Frasca
2025	J	`jnl`	Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality. CoRR Joshua Southern, Yam Eitan, Guy Bar-Shalom, Michael M. Bronstein, Haggai Maron, Fabrizio Frasca
2025	J	`jnl`	Beyond Token Probes: Hallucination Detection via Activation Tensors with ACT-ViT. CoRR Guy Bar-Shalom, Fabrizio Frasca, Yaniv Galron, Yftah Ziser, Haggai Maron
2025	J	`jnl`	FS-KAN: Permutation Equivariant Kolmogorov-Arnold Networks via Function Sharing. CoRR Ran Elbaz, Guy Bar-Shalom, Yam Eitan, Fabrizio Frasca, Haggai Maron
2025	J	`jnl`	GraphBench: Next-generation graph learning benchmarking. CoRR Timo Stoll, Chendi Qian, Ben Finkelshtein, Ali Parviz, Darius Weber, Fabrizio Frasca, Hadar Shavit, Antoine Siraudin, Arman Mielke, Marie Anastacio, Erik Müller, Maya Bechler-Speicher, Michael M. Bronstein, Mikhail Galkin, Holger H. Hoos, Mathias Niepert, Bryan Perozzi, Jan Tönshoff, Christopher Morris
2025	J	`jnl`	Learning on LLM Output Signatures for gray-box LLM Behavior Analysis. CoRR Guy Bar-Shalom, Fabrizio Frasca, Derek Lim, Yoav Gelberg, Yftah Ziser, Ran El-Yaniv, Gal Chechik, Haggai Maron
2025	J	`jnl`	Lost in Serialization: Invariance and Generalization of LLM Graph Reasoners. CoRR Daniel Herbst, Lea Karbevska, Divyanshu Kumar, Akanksha Ahuja, Fatemeh Gholamzadeh Nasrabadi, Fabrizio Frasca
2025	J	`jnl`	Neural Message-Passing on Attention Graphs for Hallucination Detection. CoRR Fabrizio Frasca, Guy Bar-Shalom, Yftah Ziser, Haggai Maron
2025	J	`jnl`	On The Expressive Power of GNN Derivatives. CoRR Yam Eitan, Moshe Eliasof, Yoav Gelberg, Fabrizio Frasca, Guy Bar-Shalom, Haggai Maron
2025	—	`conf`	Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks. ICML (Position Papers) Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca, Luis Müller, Jan Tönshoff, Antoine Siraudin, Viktor Zaverkin, Michael M. Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, Christopher Morris
2025	J	`jnl`	Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks. CoRR Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca, Luis Müller, Jan Tönshoff, Antoine Siraudin, Viktor Zaverkin, Michael M. Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, Christopher Morris
2025	A*	`conf`	Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity. ICLR Yam Eitan, Yoav Gelberg, Guy Bar-Shalom, Fabrizio Frasca, Michael M. Bronstein, Haggai Maron
2025	J	`jnl`	Understanding and Improving Laplacian Positional Encodings For Temporal GNNs. CoRR Yaniv Galron, Fabrizio Frasca, Haggai Maron, Eran Treister, Moshe Eliasof
2025	—	`conf`	Understanding and Improving Laplacian Positional Encodings for Temporal GNNs. ECML/PKDD (2) Yaniv Galron, Fabrizio Frasca, Haggai Maron, Eran Treister, Moshe Eliasof
2024	A*	`conf`	A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening. NeurIPS Guy Bar-Shalom, Yam Eitan, Fabrizio Frasca, Haggai Maron
2024	J	`jnl`	A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening. CoRR Guy Bar-Shalom, Yam Eitan, Fabrizio Frasca, Haggai Maron
2024	J	`jnl`	Future Directions in Foundations of Graph Machine Learning. CoRR Christopher Morris, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Fabrizio Frasca, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, Stefanie Jegelka
2024	A*	`conf`	Position: Future Directions in the Theory of Graph Machine Learning. ICML Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, Stefanie Jegelka
2024	J	`jnl`	Topological Blind Spots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity. CoRR Yam Eitan, Yoav Gelberg, Guy Bar-Shalom, Fabrizio Frasca, Michael M. Bronstein, Haggai Maron
2024	J	`jnl`	Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs. CoRR Fabrizio Frasca, Fabian Jogl, Moshe Eliasof, Matan Ostrovsky, Carola-Bibiane Schönlieb, Thomas Gärtner, Haggai Maron
2023	—	`conf`	Edge Directionality Improves Learning on Heterophilic Graphs. LoG Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein
2023	J	`jnl`	Edge Directionality Improves Learning on Heterophilic Graphs. CoRR Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein
2023	A*	`conf`	Graph Neural Networks for Link Prediction with Subgraph Sketching. ICLR Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Yannick Hammerla, Michael M. Bronstein, Max Hansmire
2023	A*	`conf`	Graph Positional Encoding via Random Feature Propagation. ICML Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron
2023	J	`jnl`	Graph Positional Encoding via Random Feature Propagation. CoRR Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron
2023	J	`jnl`	Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting. IEEE Trans. Pattern Anal. Mach. Intell. Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, Michael M. Bronstein
2022	J	`jnl`	Accurate and highly interpretable prediction of gene expression from histone modifications. BMC Bioinform. Fabrizio Frasca, Matteo Matteucci, Michele Leone, Marco J. Morelli, Marco Masseroli
2022	A*	`conf`	Equivariant Subgraph Aggregation Networks. ICLR Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron
2022	J	`jnl`	Graph Neural Networks for Link Prediction with Subgraph Sketching. CoRR Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Hammerla, Michael M. Bronstein, Max Hansmire
2022	A*	`conf`	Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. NeurIPS Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron
2022	J	`jnl`	Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. CoRR Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron
2021	J	`jnl`	Equivariant Subgraph Aggregation Networks. CoRR Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron
2021	A*	`conf`	Weisfeiler and Lehman Go Cellular: CW Networks. NeurIPS Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yuguang Wang, Pietro Liò, Guido F. Montúfar, Michael M. Bronstein
2021	J	`jnl`	Weisfeiler and Lehman Go Cellular: CW Networks. CoRR Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido Montúfar, Michael M. Bronstein
2021	A*	`conf`	Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. ICML Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F. Montúfar, Pietro Lió, Michael M. Bronstein
2021	J	`jnl`	Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. CoRR Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montúfar, Pietro Liò, Michael M. Bronstein
2020	J	`jnl`	Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting. CoRR Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, Michael M. Bronstein
2020	J	`jnl`	SIGN: Scalable Inception Graph Neural Networks. CoRR Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael M. Bronstein, Federico Monti
2020	J	`jnl`	Temporal Graph Networks for Deep Learning on Dynamic Graphs. CoRR Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael M. Bronstein
2019	J	`jnl`	Fake News Detection on Social Media using Geometric Deep Learning. CoRR Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, Michael M. Bronstein
2019	J	`jnl`	Learning interpretable disease self-representations for drug repositioning. CoRR Fabrizio Frasca, Diego Galeano, Guadalupe Gonzalez, Ivan Laponogov, Kirill A. Veselkov, Alberto Paccanaro, Michael M. Bronstein
2018	—	`conf`	Exposing and Characterizing Subpopulations of Distinctly Regulated Genes by K-Plane Regression. CIBB Fabrizio Frasca, Matteo Matteucci, Marco J. Morelli, Marco Masseroli
2018	B	`conf`	Modeling Gene Transcriptional Regulation by Means of Hyperplanes Genetic Clustering. IJCNN Fabrizio Frasca, Matteo Matteucci, Marco Masseroli, Marco J. Morelli