Federico Errica

Mathias Niepert

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs.

[BibT_eX]

[DOI]

J. Open Source Softw., October, 2023

PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs.

[BibT_eX]

[DOI]

Dataset, October, 2023

Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching.

[BibT_eX]

[DOI]

CoRR, 2023

Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling.

[BibT_eX]

[DOI]

Henrik Christiansen

Francesco Alesiani

CoRR, 2023

Investigating the Interplay between Features and Structures in Graph Learning.

[BibT_eX]

[DOI]

Daniele Castellana

CoRR, 2023

On Class Distributions Induced by Nearest Neighbor Graphs for Node Classification of Tabular Data.

[BibT_eX]

[DOI]

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

Hidden Markov Models for Temporal Graph Representation Learning.

[BibT_eX]

[DOI]

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

Graph Representation Learning.

[BibT_eX]

[DOI]

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

2022

Bayesian Deep Learning for Graphs.

[BibT_eX]

[DOI]

PhD thesis, 2022

Towards learning trustworthily, automatically, and with guarantees on graphs: An overview.

[BibT_eX]

[DOI]

Neurocomputing, 2022

Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark.

[BibT_eX]

[DOI]

Frontiers Artif. Intell., 2022

Bayesian Deep Learning for Graphs.

[BibT_eX]

[DOI]

CoRR, 2022

The Infinite Contextual Graph Markov Model.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Deep Learning for Graphs.

[BibT_eX]

[DOI]

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

2021

Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification.

[BibT_eX]

[DOI]

CoRR, 2021

Concept Matching for Low-Resource Classification.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2021

Modeling Edge Features with Deep Bayesian Graph Networks.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2021

Graph Mixture Density Networks.

[BibT_eX]

[DOI]

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

Complex Data: Learning Trustworthily, Automatically, and with Guarantees.

[BibT_eX]

[DOI]

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

Robust Malware Classification via Deep Graph Networks on Call Graph Topologies.

[BibT_eX]

[DOI]

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

2020

A gentle introduction to deep learning for graphs.

[BibT_eX]

[DOI]

Neural Networks, 2020

Probabilistic Learning on Graphs via Contextual Architectures.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2020

Accelerating the identification of informative reduced representations of proteins with deep learning for graphs.

[BibT_eX]

[DOI]

CoRR, 2020

A Fair Comparison of Graph Neural Networks for Graph Classification.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

Theoretically Expressive and Edge-aware Graph Learning.

[BibT_eX]

[DOI]

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

2018

Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing.

[BibT_eX]

[DOI]