Publications | Emanuele Rossi

Below is a list of my publications in reversed chronological order, with links to all the relevant resources such as arXiv, GitHub, slides, etc. An always up-to-date list of my papers is available on Google Scholar.

2024

NeurIPS

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

Julia Gastinger, Shenyang Huang, Mikhail Galkin, and 9 more authors

Advances in Neural Information Processing Systems, 2024

arXiv Bib Code Website

@article{tgb2_gastinger_2024,
  title = {TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs},
  author = {Gastinger, Julia and Huang, Shenyang and Galkin, Mikhail and Loghmani, Erfan and Parviz, Ali and Poursafaei, Farimah and Danovitch, Jacob and Rossi, Emanuele and Koutis, Ioannis and Stuckenschmidt, Heiner and Rabbany, Reihaneh and Rabusseau, Guillaume},
  year = {2024},
  journal = {Advances in Neural Information Processing Systems},
  volume = {37},
  pages = {140199-140229},
}

ACM

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

Daniele Malitesta, Emanuele Rossi, Claudio Pomo, and 2 more authors

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

arXiv Bib

@article{malitesta_multimodal_2024,
  title = {Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?},
  author = {Malitesta, Daniele and Rossi, Emanuele and Pomo, Claudio and Noia, Tommaso Di and Malliaros, Fragkiskos D.},
  year = {2024},
  journal = {Proceedings of the 33rd ACM International Conference on Information},
}

arXiv

Bayesian Binary Search

Vikash Singh, Matthew Khanzadeh, Vincent Davis, and 4 more authors

arXiv preprint, 2024

arXiv Bib

@article{singh_bayesian_2024,
  title = {Bayesian Binary Search},
  author = {Singh, Vikash and Khanzadeh, Matthew and Davis, Vincent and Rush, Harrison and Rossi, Emanuele and Shrader, Jesse and Lio, Pietro},
  year = {2024},
  journal = {arXiv preprint},
}

bioRxiv

PINDER: The Protein Interaction Dataset and Evaluation Resource

Daniel Kovtun, Mehmet Akdel, Alexander Goncearenco, and 16 more authors

bioRxiv, 2024

Bib Code Docs

@article{kovtun_pinder_2024,
  title = {PINDER: The Protein Interaction Dataset and Evaluation Resource},
  author = {Kovtun, Daniel and Akdel, Mehmet and Goncearenco, Alexander and Zhou, Guoqing and Holt, Graham and Baugher, David and Lin, Dejun and Adeshina, Yusuf and Castiglione, Thomas and Wang, Xiaoyun and Marquet, Céline and McPartlon, Matt and Geffner, Tomas and Corso, Gabriele and Stärk, Hannes and Carpenter, Zachary and Kucukbenli, Emine and Bronstein, Michael and Naef, Luca},
  year = {2024},
  journal = {bioRxiv},
  biorxiv = {2024.07.17.603980v4},
  docs = {https://pinder-org.github.io/pinder},
}

bioRxiv

PLINDER: The Protein-Ligand Interactions Dataset and Evaluation Resource

Janani Durairaj, Yusuf Adeshina, Zhonglin Cao, and 20 more authors

ICML ML for Life and Material Science Workshop, 2024

Bib Code Dataset Docs

@article{durairaj_plinder_2024,
  title = {PLINDER: The Protein-Ligand Interactions Dataset and Evaluation Resource},
  author = {Durairaj, Janani and Adeshina, Yusuf and Cao, Zhonglin and Zhang, Xuejin and Oleinikovas, Vladas and Duignan, Thomas and McClure, Zachary and Robin, Xavier and Kovtun, Danny and Rossi, Emanuele and Zhou, Guoqing and Veccham, Srimukh and Isert, Clemens and Peng, Yuxing and Sundareson, Prabindh and Akdel, Mehmet and Corso, Gabriele and Stärk, Hannes and Carpenter, Zachary and Bronstein, Michael and Kucukbenli, Emine and Schwede, Torsten and Naef, Luca},
  year = {2024},
  journal = {ICML ML for Life and Material Science Workshop},
  biorxiv = {2024.07.17.603980v4},
  dataset = {https://console.cloud.google.com/storage/browser/plinder},
  docs = {https://plinder-org.github.io/plinder},
}

arXiv

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

Shenyang Huang, Farimah Poursafaei, Reihaneh Rabbany, and 2 more authors

arXiv, 2024

arXiv Bib Video

@article{huang_utg_2024,
  title = {UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs},
  author = {Huang, Shenyang and Poursafaei, Farimah and Rabbany, Reihaneh and Rabusseau, Guillaume and Rossi, Emanuele},
  year = {2024},
  journal = {arXiv},
}

arXiv

Channel Balance Interpolation in the Lightning Network via Machine Learning

Vincent, Emanuele Rossi, and Vikash Singh

arXiv, 2024

arXiv Bib

@article{vincent_lightning_2024,
  title = {Channel Balance Interpolation in the Lightning Network via Machine Learning},
  author = {Vincent and Rossi, Emanuele and Singh, Vikash},
  year = {2024},
  journal = {arXiv},
}

2023

NeurIPS

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, and 7 more authors

Advances in Neural Information Processing Systems, 2023

arXiv Bib Code Website

@article{tgb_huang_2023,
  title = {Temporal Graph Benchmark for Machine Learning on Temporal Graphs},
  author = {Huang, Shenyang and Poursafaei, Farimah and Danovitch, Jacob and Fey, Matthias and Hu, Weihua and Rossi, Emanuele and Leskovec, Jure and Bronstein, Michael M. and Rabusseau, Guillaume and Rabbany, Reihaneh},
  year = {2023},
  journal = {Advances in Neural Information Processing Systems},
}

arXiv

Edge Directionality Improves Learning on Heterophilic Graphs

Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, and 3 more authors

Learning on Graphs Conference (LoG), 2023

arXiv Bib Blog Code

@article{dirgnn_rossi_2023,
  title = {Edge Directionality Improves Learning on Heterophilic Graphs},
  author = {Rossi, Emanuele and Charpentier, Bertrand and Giovanni, Francesco Di and Frasca, Fabrizio and Günnemann, Stephan and Bronstein, Michael M.},
  year = {2023},
  journal = {Learning on Graphs Conference (LoG)},
}

2022

ICLR

Graph Neural Networks for Link Prediction with Subgraph Sketching

Ben Chamberlain, Sergey Shirobokov, Emanuele Rossi, and 5 more authors

International Conference on Learning Representations (ICLR), 2022

arXiv Bib Video Code

@article{chamberlain2022lpsketching,
  title = {Graph Neural Networks for Link Prediction with Subgraph Sketching},
  author = {Chamberlain, Ben and Shirobokov, Sergey and Rossi, Emanuele and Frasca, Fabrizio and Markovich, Thomas and Hammerla, Nils and Bronstein, Michael M. and Hansmire, Max},
  year = {2022},
  journal = {International Conference on Learning Representations (ICLR)},
}

AAAI

Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization

Mirco Mutti, Riccardo De Santi, Emanuele Rossi, and 3 more authors

Proceedings of the AAAI Conference on Artificial Intelligence, 2022

arXiv Bib

@article{mutti2022causalrl,
  title = {Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization},
  author = {Mutti, Mirco and Santi, Riccardo De and Rossi, Emanuele and Calderon, Juan Felipe and Bronstein, Michael M. and Restelli, Marcello},
  year = {2022},
  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
}

ICML

Learning to Infer Structures of Network Games

Emanuele Rossi, Federico Monti, Yan Leng, and 2 more authors

Proceedings of the 39th International Conference on Machine Learning, ICML, 2022

arXiv Bib Blog

@article{rossi2022networkgames,
  title = {Learning to Infer Structures of Network Games},
  author = {Rossi, Emanuele and Monti, Federico and Leng, Yan and Bronstein, Michael M. and Dong, Xiaowen},
  year = {2022},
  journal = {Proceedings of the 39th International Conference on Machine Learning,
                 {ICML}},
}

LoG

On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features

Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, and 3 more authors

Learning on Graphs Conference (LoG), 2022

arXiv Bib Blog Code Slides

@article{rossi2021fp,
  title = {On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features},
  author = {Rossi, Emanuele and Kenlay, Henry and Gorinova, Maria I. and Chamberlain, Ben and Dong, Xiaowen and Bronstein, Michael M.},
  year = {2022},
  journal = {Learning on Graphs Conference (LoG)},
}

2021

ICML

GRAND: Graph Neural Diffusion

Ben Chamberlain, James Rowbottom, Maria I. Gorinova, and 3 more authors

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

arXiv Bib Blog Code

@article{chamberlain2021grand,
  title = {GRAND: Graph Neural Diffusion},
  author = {Chamberlain, Ben and Rowbottom, James and Gorinova, Maria I. and Webb, Stefan D. and Rossi, Emanuele and Bronstein, Michael M.},
  year = {2021},
  journal = {Proceedings of the 38th International Conference on Machine Learning,
                 {ICML}},
}

2020

RecSys

Tuning Word2vec for Large Scale Recommendation Systems

Ben Chamberlain, Emanuele Rossi, Dan Shiebler, and 2 more authors

RecSys - 14th ACM Conference on Recommender Systems, 2020

arXiv Bib

@article{chamberlain2020word2vec,
  title = {Tuning Word2vec for Large Scale Recommendation Systems},
  author = {Chamberlain, Ben and Rossi, Emanuele and Shiebler, Dan and Sedhain, Suvash and Bronstein, Michael},
  year = {2020},
  journal = {RecSys - 14th ACM Conference on Recommender Systems},
}

ICML

Temporal Graph Networks for Deep Learning on Dynamic Graphs

Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, and 3 more authors

ICML Workshop on Graph Representation Learning, 2020

arXiv Bib Blog Code Slides

@article{rossi2020tgn,
  title = {Temporal Graph Networks for Deep Learning on Dynamic Graphs},
  author = {Rossi, Emanuele and Chamberlain, Ben and Frasca, Fabrizio and Eynard, Davide and Monti, Federico and Bronstein, Michael},
  year = {2020},
  journal = {ICML Workshop on Graph Representation Learning},
}

ICML

SIGN: Scalable Inception Graph Neural Networks

Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, and 3 more authors

ICML Workshop on Graph Representation Learning, 2020

arXiv Bib Blog Code

@article{rossi2020sign,
  title = {SIGN: Scalable Inception Graph Neural Networks},
  author = {Rossi, Emanuele and Frasca, Fabrizio and Chamberlain, Ben and Eynard, Davide and Bronstein, Michael and Monti, Federico},
  year = {2020},
  journal = {ICML Workshop on Graph Representation Learning},
}

2019

KDD

ncRNA Classification with Graph Convolutional Networks

Emanuele Rossi, Federico Monti, Michael Bronstein, and 1 more author

KDD Workshop on Deep Learning on Graphs, 2019

arXiv Bib Code

@article{rossi2019ncrna,
  title = {ncRNA Classification with Graph Convolutional Networks},
  author = {Rossi, Emanuele and Monti, Federico and Bronstein, Michael and Li{\`o}, Pietro},
  journal = {KDD Workshop on Deep Learning on Graphs},
  year = {2019},
}