Publications

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2021

Bacciu, Davide; Bianchi, Filippo Maria; Paassen, Benjamin; Alippi, Cesare

Deep learning for graphs Conference Forthcoming

Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021), Forthcoming.

BibTeX | Tags: deep learning, deep learning for graphs, graph data, structured data processing

Dukic, Haris; Deligiorgis, Georgios; Sepe, Pierpaolo; Bacciu, Davide; Trincavelli, Marco

Inductive learning for product assortment graph completion Conference Forthcoming

Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021), Forthcoming.

BibTeX | Tags: deep learning for graphs, graph data, recommendation systems

Bacciu, Davide; Conte, Alessio; Grossi, Roberto; Landolfi, Francesco; Marino, Andrea

K-Plex Cover Pooling for Graph Neural Networks Journal Article

Data Mining and Knowledge Discovery, 2021, (Accepted also as paper to the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2021)).

Abstract | Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, graph pooling, structured data processing

Atzeni, Daniele; Bacciu, Davide; Errica, Federico; Micheli, Alessio

Modeling Edge Features with Deep Bayesian Graph Networks Conference Forthcoming

Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE Forthcoming.

BibTeX | Tags: deep learning for graphs, generative model, hidden Markov models, structured data processing

Numeroso, Danilo; Bacciu, Davide

MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks Conference Forthcoming

Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE Forthcoming.

BibTeX | Tags: deep learning for graphs, explainable AI, graph data, structured data processing

Bacciu, Davide; Podda, Marco

GraphGen-Redux: a Fast and Lightweight Recurrent Model for Labeled Graph Generation Conference Forthcoming

Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE Forthcoming.

BibTeX | Tags: deep learning, deep learning for graphs, generative model, graph data

Errica, Federico; Bacciu, Davide; Micheli, Alessio

Graph Mixture Density Networks Conference Forthcoming

Proceedings of the 38th International Conference on Machine Learning (ICML 2021), Forthcoming.

BibTeX | Tags: deep learning for graphs, generative model, graph data, structured data processing

Sattar, Asma; Bacciu, Davide

Context-aware Graph Convolutional Autoencoder Conference Forthcoming

Proceedings of the 16th International Work Conference on Artificial Neural Networks (IWANN 2021), Forthcoming.

BibTeX | Tags: deep learning for graphs, graph data, recommendation systems

Carta, Antonio; Cossu, Andrea; Errica, Federico; Bacciu, Davide

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

The Web Conference 2021 Workshop on Graph Learning Benchmarks (GLB21), 2021.

Abstract | BibTeX | Tags: Continual learning, deep learning for graphs, structured data processing

Errica, Federico; Giulini, Marco; Bacciu, Davide; Menichetti, Roberto; Micheli, Alessio; Potestio, Raffaello

A deep graph network-enhanced sampling approach to efficiently explore the space of reduced representations of proteins Journal Article

Frontiers in Molecular Biosciences, 8 , pp. 136, 2021.

Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, structured data processing

2020

Bacciu, Davide; Conte, Alessio; Grossi, Roberto; Landolfi, Francesco; Marino, Andrea

K-plex Cover Pooling for Graph Neural Networks Workshop

34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Learning Meets Combinatorial Algorithms, 2020.

Abstract | BibTeX | Tags: deep learning, deep learning for graphs, graph data, graph pooling, structured data processing

Bacciu, Davide; Numeroso, Danilo

Explaining Deep Graph Networks with Molecular Counterfactuals Workshop

34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Machine Learning for Molecules - Accepted as Contributed Talk (Oral), 2020.

Abstract | Links | BibTeX | Tags: deep learning for graphs, explainable AI, graph data, structured data processing

Bacciu, Davide; Errica, Federico; Micheli, Alessio; Podda, Marco

A Gentle Introduction to Deep Learning for Graphs Journal Article

Neural Networks, 129 , pp. 203-221, 2020.

Abstract | Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, structured data processing

Bacciu, Davide; Errica, Federico; Micheli, Alessio

Probabilistic Learning on Graphs via Contextual Architectures Journal Article

Journal of Machine Learning Research, 21 (134), pp. 1−39, 2020.

Abstract | Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing

A Deep Generative Model for Fragment-Based Molecule Generation

Podda, Marco; Bacciu, Davide; Micheli, Alessio

A Deep Generative Model for Fragment-Based Molecule Generation Conference

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020) , 2020.

Abstract | Links | BibTeX | Tags: deep learning for graphs, generative model, graph data, molecule generation, recurrent neural network, structured data processing

A Fair Comparison of Graph Neural Networks for Graph Classification

Errica, Federico; Podda, Marco; Bacciu, Davide; Micheli, Alessio

A Fair Comparison of Graph Neural Networks for Graph Classification Conference

Proceedings of the Eighth International Conference on Learning Representations (ICLR 2020), 2020.

Abstract | Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, structured data processing

 Biochemical Pathway Robustness Prediction with Graph Neural Networks

Podda, Marco; Micheli, Alessio; Bacciu, Davide; Milazzo, Paolo

Biochemical Pathway Robustness Prediction with Graph Neural Networks Conference

Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.

BibTeX | Tags: bioinformatics, biomedical data, deep learning for graphs, structured data processing

 Theoretically Expressive and Edge-aware Graph Learning

Errica, Federico; Bacciu, Davide; Micheli, Alessio

Theoretically Expressive and Edge-aware Graph Learning Conference

Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.

Abstract | Links | BibTeX | Tags: deep learning for graphs, structured data processing

Deep Learning for Graphs

Bacciu, Davide; Micheli, Alessio

Deep Learning for Graphs Book Chapter

Oneto, Luca; Navarin, Nicolo; Sperduti, Alessandro; Anguita, Davide (Ed.): Recent Trends in Learning From Data: Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019), 896 , pp. 99-127, Springer International Publishing, 2020, ISBN: 978-3-030-43883-8.

Abstract | Links | BibTeX | Tags: deep learning for graphs, generative model, graph data, molecule generation, recurrent neural network, structured data processing

2019

Edge-based sequential graph generation with recurrent neural networks

Bacciu, Davide; Micheli, Alessio; Podda, Marco

Edge-based sequential graph generation with recurrent neural networks Journal Article

Neurocomputing, 2019.

Abstract | Links | BibTeX | Tags: deep learning for graphs, generative model, graph data, structured data processing

A non-negative factorization approach to node pooling in graph convolutional neural networks

Bacciu, Davide; Di Sotto, Luigi

A non-negative factorization approach to node pooling in graph convolutional neural networks Conference

Proceedings of the 18th International Conference of the Italian Association for Artificial Intelligence (AIIA 2019), Lecture Notes in Artificial Intelligence Springer-Verlag, 2019.

Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing

Graph generation by sequential edge prediction

Bacciu, Davide; Micheli, Alessio; Podda, Marco

Graph generation by sequential edge prediction Conference

Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'19), i6doc.com, Louvain-la-Neuve, Belgium, 2019.

Links | BibTeX | Tags: deep learning for graphs, generative model, graph data, structured data processing

2018

Text Summarization as Tree Transduction by Top-Down TreeLSTM

Davide, Bacciu; Antonio, Bruno

Text Summarization as Tree Transduction by Top-Down TreeLSTM Conference

Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI'18), IEEE, 2018.

Abstract | Links | BibTeX | Tags: deep learning, deep learning for graphs, neural networks, structured data processing, tree structured data, tree transductions

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

Davide, Bacciu; Federico, Errica; Alessio, Micheli

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

Proceedings of the 35th International Conference on Machine Learning (ICML 2018), 2018.

Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing