Nicolò Navarin

ORCID

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 83

Top Venues

University of Bologna, Italy (PHD)

Education History

University of Bologna, Italy

PHD

Publications

Paolo Frazzetto, Luca Pasa, Nicolò Navarin, Alessandro Sperduti
Beyond the Additive Nodes' Convolutions: a Study on High-Order Multiplicative Integration. SAC (2024)
Luca Pasa, Nicolò Navarin, Wolfgang Erb, Alessandro Sperduti
A unified framework for backpropagation-free soft and hard gated graph neural networks. Knowl. Inf. Syst. 66 (4) (2024)
Danilo Franco, Vincenzo Stefano D'Amato, Luca Pasa, Nicolò Navarin, Luca Oneto
Fair graph representation learning: Empowering NIFTY via Biased Edge Dropout and Fair Attribute Preprocessing. Neurocomputing 563 (2024)
Giovanni Donghi, Luca Pasa, Luca Oneto, Claudio Gallicchio, Alessio Micheli, Davide Anguita, Alessandro Sperduti, Nicolò Navarin
Investigating over-parameterized randomized graph networks. Neurocomputing 606 (2024)
Luca Pasa, Nicolò Navarin, Wolfgang Erb, Alessandro Sperduti
Empowering Simple Graph Convolutional Networks. IEEE Trans. Neural Networks Learn. Syst. 35 (4) (2024)
Alvise De Biasio, Dietmar Jannach, Nicolò Navarin
Model-based approaches to profit-aware recommendation. Expert Syst. Appl. 249 (2024)
Alvise De Biasio, Nicolò Navarin, Dietmar Jannach
Economic recommender systems - a systematic review. Electron. Commer. Res. Appl. 63 (2024)
Nicolò Navarin, Dounia Mulders, Luca Oneto
Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing 571 (2024)
Alvise De Biasio, Andrea Montagna, Fabio Aiolli, Nicolò Navarin
A systematic review of value-aware recommender systems. Expert Syst. Appl. 226 (2023)
Valentina Fietta, Nicolò Navarin, Merylin Monaro, Ombretta Gaggi
Women and Gender Disparities in Computer Science: A Case Study at the University of Padua. GoodIT (2023)
Paolo Frazzetto, Luca Pasa, Nicolò Navarin, Alessandro Sperduti
Topology preserving maps as aggregations for Graph Convolutional Neural Networks. SAC (2023)
Alvise De Biasio, Nicolò Navarin, Dietmar Jannach
Economic Recommender Systems - A Systematic Review. CoRR (2023)
Riccardo Galanti, Massimiliano de Leoni, Merylin Monaro, Nicolò Navarin, Alan Marazzi, Brigida Di Stasi, Stéphanie Maldera
An explainable decision support system for predictive process analytics. Eng. Appl. Artif. Intell. 120 (2023)
Davide Rigoni, Nicolò Navarin, Alessandro Sperduti
RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design. CoRR (2023)
Alvise De Biasio, Merylin Monaro, Luca Oneto, Lamberto Ballan, Nicolò Navarin
On the problem of recommendation for sensitive users and influential items: Simultaneously maintaining interest and diversity. Knowl. Based Syst. 275 (2023)
Andrea Montagna, Alvise De Biasio, Nicolò Navarin, Fabio Aiolli
Graph-based Explainable Recommendation Systems: Are We Rigorously Evaluating Explanations? HCAI4U@CHItaly (2023)
Davide Bacciu, Federico Errica, Alessio Micheli, Nicolò Navarin, Luca Pasa, Marco Podda, Daniele Zambon
Graph Representation Learning. ESANN (2023)
Nicolò Navarin, Luca Pasa, Luca Oneto, Alessandro Sperduti
An Empirical Study of Over-Parameterized Neural Models based on Graph Random Features. ESANN (2023)
Riccardo Galanti, Massimiliano de Leoni, Nicolò Navarin, Alan Marazzi
Object-centric process predictive analytics. Expert Syst. Appl. 213 (Part) (2023)
Luca Pasa, Nicolò Navarin, Wolfgang Erb, Alessandro Sperduti
Backpropagation-free Graph Neural Networks. ICDM (2022)
Merylin Monaro, Emilia I. Barakova, Nicolò Navarin
Editorial Special Issue Interaction With Artificial Intelligence Systems: New Human-Centered Perspectives and Challenges. IEEE Trans. Hum. Mach. Syst. 52 (3) (2022)
Davide Bacciu, Federico Errica, Nicolò Navarin, Luca Pasa, Daniele Zambon
Deep Learning for Graphs. ESANN (2022)
Danilo Franco, Nicolò Navarin, Michele Donini, Davide Anguita, Luca Oneto
Deep fair models for complex data: Graphs labeling and explainable face recognition. Neurocomputing 470 (2022)
Luca Oneto, Nicolò Navarin, Frank-Michael Schleif
Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing 507 (2022)
Luca Pasa, Nicolò Navarin, Alessandro Sperduti
Multiresolution Reservoir Graph Neural Network. IEEE Trans. Neural Networks Learn. Syst. 33 (6) (2022)
Luca Oneto, Kerstin Bunte, Nicolò Navarin
Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing 470 (2022)
Luca Pasa, Nicolò Navarin, Alessandro Sperduti
Polynomial-based graph convolutional neural networks for graph classification. Mach. Learn. 111 (4) (2022)
Luca Pasa, Nicolò Navarin, Alessandro Sperduti
Compact graph neural network models for node classification. SAC (2022)
Riccardo Galanti, Massimiliano de Leoni, Nicolò Navarin, Alan Marazzi
Object-centric Process Predictive Analytics. CoRR (2022)
Luca Pasa, Nicolò Navarin, Alessandro Sperduti
SOM-based aggregation for graph convolutional neural networks. Neural Comput. Appl. 34 (1) (2022)
Merylin Monaro, Valentina Fietta, Valentina Curró, Giulia Lusetti, Giuseppe Sartori, Nicolò Navarin
Forged handwriting verification: a public domain dataset for training machine learning models. IJCNN (2022)
Federico Caldart, Luca Pasa, Luca Oneto, Alessandro Sperduti, Nicolò Navarin
Biased Edge Dropout in NIFTY for Fair Graph Representation Learning. ESANN (2022)
Matteo Munari, Luca Pasa, Daniele Zambon, Cesare Alippi, Nicolò Navarin
Understanding Catastrophic Forgetting of Gated Linear Networks in Continual Learning. IJCNN (2022)
Luca Oneto, Nicolò Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Luca Demetrio, Pietro Bongini, Armando Tacchella, Alessandro Sperduti
Towards learning trustworthily, automatically, and with guarantees on graphs: An overview. Neurocomputing 493 (2022)
Merylin Monaro, Stéphanie Maldera, Cristina Scarpazza, Giuseppe Sartori, Nicolò Navarin
Detecting deception through facial expressions in a dataset of videotaped interviews: A comparison between human judges and machine learning models. Comput. Hum. Behav. 127 (2022)
Matteo Cardaioli, Alessio Miolla, Mauro Conti, Giuseppe Sartori, Merylin Monaro, Cristina Scarpazza, Nicolò Navarin
Face the Truth: Interpretable Emotion Genuineness Detection. IJCNN (2022)
Riccardo Galanti, Massimiliano de Leoni, Merylin Monaro, Nicolò Navarin, Alan Marazzi, Brigida Di Stasi, Stéphanie Maldera
An Explainable Decision Support System for Predictive Process Analytics. CoRR (2022)
Luca Oneto, Nicolò Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Alessandro Sperduti
Complex Data: Learning Trustworthily, Automatically, and with Guarantees. ESANN (2021)
Luca Pasa, Nicolò Navarin, Alessandro Sperduti
Tangent Graph Convolutional Network. ESANN (2021)
Danilo Franco, Luca Oneto, Nicolò Navarin, Davide Anguita
Learn and Visually Explain Deep Fair Models: an Application to Face Recognition. IJCNN (2021)
Luca Pasa, Nicolò Navarin, Alessandro Sperduti
Simple Multi-resolution Gated GNN. SSCI (2021)
Danilo Franco, Luca Oneto, Nicolò Navarin, Davide Anguita
Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition. Entropy 23 (8) (2021)
Luca Pasa, Nicolò Navarin, Wolfgang Erb, Alessandro Sperduti
Simple Graph Convolutional Networks. CoRR (2021)
Mirko Polato, Denys Demchenko, Almat Kuanyshkereyev, Nicolò Navarin
Efficient Multilingual Deep Learning Model for Keyword Categorization. SSCI (2021)
Nicolò Navarin, Dinh Tran-Van, Alessandro Sperduti
A framework for the definition of complex structured feature spaces. Neurocomputing 416 (2020)
Riccardo Galanti, Bernat Coma-Puig, Massimiliano de Leoni, Josep Carmona, Nicolò Navarin
Explainable Predictive Process Monitoring. ICPM (2020)
Luca Pasa, Nicolò Navarin, Alessandro Sperduti
Deep Recurrent Graph Neural Networks. ESANN (2020)
Giorgio Nicola, Luca Tagliapietra, Elisa Tosello, Nicolò Navarin, Stefano Ghidoni, Emanuele Menegatti
Robotic Object Sorting via Deep Reinforcement Learning: a generalized approach. RO-MAN (2020)
Luca Oneto, Nicolò Navarin, Michele Donini
Learning Deep Fair Graph Neural Networks. ESANN (2020)
Riccardo Galanti, Bernat Coma-Puig, Massimiliano de Leoni, Josep Carmona, Nicolò Navarin
Explainable Predictive Process Monitoring. CoRR (2020)
Davide Rigoni, Nicolò Navarin, Alessandro Sperduti
Conditional Constrained Graph Variational Autoencoders for Molecule Design. CoRR (2020)
Davide Rigoni, Nicolò Navarin, Alessandro Sperduti
A Systematic Assessment of Deep Learning Models for Molecule Generation. CoRR (2020)
Nicolò Navarin, Matteo Cambiaso, Andrea Burattin, Fabrizio Maria Maggi, Luca Oneto, Alessandro Sperduti
Towards Online Discovery of Data-Aware Declarative Process Models from Event Streams. IJCNN (2020)
Manuel Dorado-Moreno, Nicolò Navarin, Pedro Antonio Gutiérrez, Luis Prieto, Alessandro Sperduti, Sancho Salcedo-Sanz, César Hervás-Martínez
Multi-task learning for the prediction of wind power ramp events with deep neural networks. Neural Networks 123 (2020)
Davide Rigoni, Nicolò Navarin, Alessandro Sperduti
A Systematic Assessment of Deep Learning Models for Molecule Generation. ESANN (2020)
Davide Rigoni, Nicolò Navarin, Alessandro Sperduti
Conditional Constrained Graph Variational Autoencoders for Molecule Design. SSCI (2020)
Nicolò Navarin, Dinh Van Tran, Alessandro Sperduti
Learning Kernel-Based Embeddings in Graph Neural Networks. ECAI (2020)
Nicolò Navarin, Wolfgang Erb, Luca Pasa, Alessandro Sperduti
Linear Graph Convolutional Networks. ESANN (2020)
Nicolò Navarin, Dinh Van Tran, Alessandro Sperduti
Universal Readout for Graph Convolutional Neural Networks. IJCNN (2019)
Nicolò Navarin, Dinh Van Tran, Alessandro Sperduti
On the definition of complex structured feature spaces. ESANN (2019)
Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita
Introduction. INNSBDDL (Tutorials) (2019)
Luca Oneto, Nicolò Navarin, Michele Donini, Sandro Ridella, Alessandro Sperduti, Fabio Aiolli, Davide Anguita
Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels. IEEE Trans. Neural Networks Learn. Syst. 29 (10) (2018)
Dinh Van Tran, Nicolò Navarin, Alessandro Sperduti
On Filter Size in Graph Convolutional Networks. CoRR (2018)
Nicolò Navarin, Giovanni Da San Martino, Alessandro Sperduti
Extreme Graph Kernels for Online Learning on a Memory Budget. IJCNN (2018)
Nicolò Navarin, Dinh Van Tran, Alessandro Sperduti
Pre-training Graph Neural Networks with Kernels. CoRR (2018)
Dinh Van Tran, Nicolò Navarin, Alessandro Sperduti
On Filter Size in Graph Convolutional Networks. SSCI (2018)
Dinh Tran-Van, Nicolò Navarin, Alessandro Sperduti
DEEP: decomposition feature enhancement procedure for graphs. ESANN (2018)
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
Tree-Based Kernel for Graphs With Continuous Attributes. IEEE Trans. Neural Networks Learn. Syst. 29 (7) (2018)
Luca Oneto, Nicolò Navarin, Michele Donini, Davide Anguita
Emerging trends in machine learning: beyond conventional methods and data. ESANN (2018)
Guido Zampieri, Dinh Tran-Van, Michele Donini, Nicolò Navarin, Fabio Aiolli, Alessandro Sperduti, Giorgio Valle
Scuba: scalable kernel-based gene prioritization. BMC Bioinform. 19 (1) (2018)
Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita
Multilayer Graph Node Kernels: Stacking While Maintaining Convexity. Neural Process. Lett. 48 (2) (2018)
Nicolò Navarin, Beatrice Vincenzi, Mirko Polato, Alessandro Sperduti
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances. CoRR (2017)
Nicolò Navarin, Alessandro Sperduti
Approximated Neighbours MinHash Graph Node Kernel. ESANN (2017)
Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita
Deep graph node kernels: A convex approach. IJCNN (2017)
Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita
Measuring the expressivity of graph kernels through Statistical Learning Theory. Neurocomputing 268 (2017)
Nicolò Navarin, Beatrice Vincenzi, Mirko Polato, Alessandro Sperduti
LSTM networks for data-aware remaining time prediction of business process instances. SSCI (2017)
Michele Donini, Nicolò Navarin, Ivano Lauriola, Fabio Aiolli, Fabrizio Costa
Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning. ESANN (2017)
Nicolò Navarin, Fabrizio Costa
An efficient graph kernel method for non-coding RNA functional prediction. Bioinform. 33 (17) (2017)
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
An empirical study on budget-aware online kernel algorithms for streams of graphs. Neurocomputing 216 (2016)
Carlo M. Massimo, Nicolò Navarin, Alessandro Sperduti
Hyper-Parameter Tuning for Graph Kernels via Multiple Kernel Learning. ICONIP (2) (2016)
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
Ordered Decompositional DAG kernels enhancements. Neurocomputing 192 (2016)
Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita
Measuring the Expressivity of Graph Kernels through the Rademacher Complexity. ESANN (2016)
Luca Oneto, Nicolò Navarin, Michele Donini, Fabio Aiolli, Davide Anguita
Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints. ESANN (2016)
Nicolò Navarin, Alessandro Sperduti, Riccardo Tesselli
Extending local features with contextual information in graph kernels. CoRR (2015)
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
Exploiting the ODD framework to define a novel effective graph kernel. ESANN (2015)
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
Graph Kernels exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions. CoRR (2015)
Fabio Aiolli, Michele Donini, Nicolò Navarin, Alessandro Sperduti
Multiple Graph-Kernel Learning. SSCI (2015)
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
An Empirical Study on Budget-Aware Online Kernel Algorithms for Streams of Graphs. CoRR (2015)
Nicolò Navarin, Alessandro Sperduti, Riccardo Tesselli
Extending Local Features with Contextual Information in Graph Kernels. ICONIP (4) (2015)
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
A tree-based kernel for graphs with continuous attributes. CoRR (2015)
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
Ordered Decompositional DAG Kernels Enhancements. CoRR (2015)
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
Graph Kernels Exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions. ICONIP (2) (2014)
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
A Lossy Counting Based Approach for Learning on Streams of Graphs on a Budget. IJCAI (2013)
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
A memory efficient graph kernel. IJCNN (2012)
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
A Tree-Based Kernel for Graphs. SDM (2012)

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Publication Activity (10 Years)

Top Topics

Top Venues

Nicolò Navarin