Emanuele Rossi

According to our database, Emanuele Rossi authored at least 19 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Training-Free Graph-Based Imputation of Missing Modalities in Multimodal Recommendation.

Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Tommaso Di Noia, Fragkiskos D. Malliaros

IEEE Trans. Knowl. Data Eng., May, 2026

Predicting Channel Closures in the Lightning Network with Machine Learning.

Simone Antonelli, Vincent Davis, Harrison Rush, Anthony Potdevin, Jesse Shrader, Vikash Singh, Emanuele Rossi

CoRR, May, 2026

2025

Deep Learning on Real-World Graphs.

Emanuele Rossi

CoRR, October, 2025

Bayesian Binary Search.

Vikash Singh, Matthew Khanzadeh, Vincent Davis, Harrison Rush, Emanuele Rossi, Jesse Shrader, Pietro Lio'

Algorithms, 2025

2024

Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach.

Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Fragkiskos D. Malliaros, Tommaso Di Noia

CoRR, 2024

TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs.

Julia Gastinger, Shenyang Huang, Michael Galkin, Erfan Loghmani, Ali Parviz, Farimah Poursafaei, Jacob Danovitch, Emanuele Rossi, Ioannis Koutis, Heiner Stuckenschmidt, Reihaneh Rabbany, Guillaume Rabusseau

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs.

Shenyang Huang, Farimah Poursafaei, Reihaneh Rabbany, Guillaume Rabusseau, Emanuele Rossi

Proceedings of the Learning on Graphs Conference, 26-29 November 2024, Virtual., 2024

Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?

Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Tommaso Di Noia, Fragkiskos D. Malliaros

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2023

Temporal Graph Benchmark for Machine Learning on Temporal Graphs.

Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael M. Bronstein, Guillaume Rabusseau, Reihaneh Rabbany

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Edge Directionality Improves Learning on Heterophilic Graphs.

Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein

Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization.

Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderón, Michael M. Bronstein, Marcello Restelli

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs With Missing Node Features.

Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein

Proceedings of the Learning on Graphs Conference, 2022

Learning to Infer Structures of Network Games.

Emanuele Rossi, Federico Monti, Yan Leng, Michael M. Bronstein, Xiaowen Dong

Proceedings of the International Conference on Machine Learning, 2022

2021

GRAND: Graph Neural Diffusion.

Ben Chamberlain, James Rowbottom, Maria I. Gorinova, Michael M. Bronstein, Stefan Webb, Emanuele Rossi

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Temporal Graph Networks for Deep Learning on Dynamic Graphs.

Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael M. Bronstein

CoRR, 2020

SIGN: Scalable Inception Graph Neural Networks.

Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael M. Bronstein, Federico Monti

CoRR, 2020

Tuning Word2vec for Large Scale Recommendation Systems.

Benjamin Paul Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

2019

ncRNA Classification with Graph Convolutional Networks.

Emanuele Rossi, Federico Monti, Michael M. Bronstein, Pietro Liò

CoRR, 2019