A memory efficient graph kernel. - Giovanni Da San Martino; Nicolò Navarin; Alessandro Sperduti (IJCNN, 2012)

A Tree-Based Kernel for Graphs. - Giovanni Da San Martino; Nicolò Navarin; Alessandro Sperduti (SDM, 2012)

A Lossy Counting Based Approach for Learning on Streams of Graphs on a Budget. - Giovanni Da San Martino; Nicolò Navarin; Alessandro Sperduti (IJCAI, 2013)

Graph Kernels Exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions. - Giovanni Da San Martino; Nicolò Navarin; Alessandro Sperduti (ICONIP (2), 2014)

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction. - Nicolò Navarin (2014)

Exploiting the ODD framework to define a novel effective graph kernel. - Giovanni Da San Martino; Nicolò Navarin; Alessandro Sperduti (ESANN, 2015)

Extending Local Features with Contextual Information in Graph Kernels. - Nicolò Navarin; Alessandro Sperduti; Riccardo Tesselli (ICONIP (4), 2015)

Multiple Graph-Kernel Learning. - Fabio Aiolli; Michele Donini; Nicolò Navarin; Alessandro Sperduti (SSCI, 2015)

Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints. - Luca Oneto; Nicolò Navarin; Michele Donini; Fabio Aiolli; Davide Anguita (ESANN, 2016)

Measuring the Expressivity of Graph Kernels through the Rademacher Complexity. - Luca Oneto; Nicolò Navarin; Michele Donini; Alessandro Sperduti; Fabio Aiolli; Davide Anguita (ESANN, 2016)

Hyper-Parameter Tuning for Graph Kernels via Multiple Kernel Learning. - Carlo M. Massimo; Nicolò Navarin; Alessandro Sperduti (ICONIP (2), 2016)

Ordered Decompositional DAG kernels enhancements. - Giovanni Da San Martino; Nicolò Navarin; Alessandro Sperduti (Neurocomputing, 2016)

An empirical study on budget-aware online kernel algorithms for streams of graphs. - Giovanni Da San Martino; Nicolò Navarin; Alessandro Sperduti (Neurocomputing, 2016)

Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning. - Michele Donini; Nicolò Navarin; Ivano Lauriola; Fabio Aiolli; Fabrizio Costa (ESANN, 2017)

Approximated Neighbours MinHash Graph Node Kernel. - Nicolò Navarin; Alessandro Sperduti (ESANN, 2017)

Deep graph node kernels: A convex approach. - Luca Oneto; Nicolò Navarin; Alessandro Sperduti; Davide Anguita (IJCNN, 2017)

LSTM networks for data-aware remaining time prediction of business process instances. - Nicolò Navarin; Beatrice Vincenzi; Mirko Polato; Alessandro Sperduti (SSCI, 2017)

An efficient graph kernel method for non-coding RNA functional prediction. - Nicolò Navarin; Fabrizio Costa (Bioinform., 2017)

Measuring the expressivity of graph kernels through Statistical Learning Theory. - Luca Oneto; Nicolò Navarin; Michele Donini; Alessandro Sperduti; Fabio Aiolli; Davide Anguita (Neurocomputing, 2017)

Emerging trends in machine learning: beyond conventional methods and data. - Luca Oneto; Nicolò Navarin; Michele Donini; Davide Anguita (ESANN, 2018)

DEEP: decomposition feature enhancement procedure for graphs. - Dinh Tran-Van; Nicolò Navarin; Alessandro Sperduti (ESANN, 2018)

Extreme Graph Kernels for Online Learning on a Memory Budget. - Nicolò Navarin; Giovanni Da San Martino; Alessandro Sperduti (IJCNN, 2018)

On Filter Size in Graph Convolutional Networks. - Dinh Van Tran; Nicolò Navarin; Alessandro Sperduti (SSCI, 2018)

Scuba: scalable kernel-based gene prioritization. - Guido Zampieri; Dinh Tran-Van; Michele Donini; Nicolò Navarin; Fabio Aiolli; Alessandro Sperduti; Giorgio Valle (BMC Bioinform., 2018)

Pre-training Graph Neural Networks with Kernels. - Nicolò Navarin; Dinh Van Tran; Alessandro Sperduti (CoRR, 2018)

Multilayer Graph Node Kernels: Stacking While Maintaining Convexity. - Luca Oneto; Nicolò Navarin; Alessandro Sperduti; Davide Anguita (Neural Process. Lett., 2018)

Tree-Based Kernel for Graphs With Continuous Attributes. - Giovanni Da San Martino; Nicolò Navarin; Alessandro Sperduti (IEEE Trans. Neural Networks Learn. Syst., 2018)

Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels. - Luca Oneto; Nicolò Navarin; Michele Donini; Sandro Ridella; Alessandro Sperduti; Fabio Aiolli; Davide Anguita (IEEE Trans. Neural Networks Learn. Syst., 2018)

On the definition of complex structured feature spaces. - Nicolò Navarin; Dinh Van Tran; Alessandro Sperduti (ESANN, 2019)

Universal Readout for Graph Convolutional Neural Networks. - Nicolò Navarin; Dinh Van Tran; Alessandro Sperduti (IJCNN, 2019)

Introduction. - Luca Oneto; Nicolò Navarin; Alessandro Sperduti; Davide Anguita (INNSBDDL (Tutorials), 2019)

Learning Kernel-Based Embeddings in Graph Neural Networks. - Nicolò Navarin; Dinh Van Tran; Alessandro Sperduti (ECAI, 2020)

Linear Graph Convolutional Networks. - Nicolò Navarin; Wolfgang Erb; Luca Pasa; Alessandro Sperduti (ESANN, 2020)

Learning Deep Fair Graph Neural Networks. - Luca Oneto; Nicolò Navarin; Michele Donini (ESANN, 2020)

Deep Recurrent Graph Neural Networks. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (ESANN, 2020)

A Systematic Assessment of Deep Learning Models for Molecule Generation. - Davide Rigoni 001; Nicolò Navarin; Alessandro Sperduti (ESANN, 2020)

Explainable Predictive Process Monitoring. - Riccardo Galanti; Bernat Coma-Puig; Massimiliano de Leoni; Josep Carmona 001; Nicolò Navarin (ICPM, 2020)

Towards Online Discovery of Data-Aware Declarative Process Models from Event Streams. - Nicolò Navarin; Matteo Cambiaso; Andrea Burattin; Fabrizio Maria Maggi; Luca Oneto; Alessandro Sperduti (IJCNN, 2020)

Robotic Object Sorting via Deep Reinforcement Learning: a generalized approach. - Giorgio Nicola; Luca Tagliapietra; Elisa Tosello; Nicolò Navarin; Stefano Ghidoni; Emanuele Menegatti (RO-MAN, 2020)

Conditional Constrained Graph Variational Autoencoders for Molecule Design. - Davide Rigoni 001; Nicolò Navarin; Alessandro Sperduti (SSCI, 2020)

A framework for the definition of complex structured feature spaces. - Nicolò Navarin; Dinh Tran-Van; Alessandro Sperduti (Neurocomputing, 2020)

Multi-task learning for the prediction of wind power ramp events with deep neural networks. - Manuel Dorado-Moreno; Nicolò Navarin; Pedro Antonio Gutiérrez; Luis Prieto; Alessandro Sperduti; Sancho Salcedo-Sanz; César Hervás-Martínez (Neural Networks, 2020)

Complex Data: Learning Trustworthily, Automatically, and with Guarantees. - Luca Oneto; Nicolò Navarin; Battista Biggio; Federico Errica; Alessio Micheli; Franco Scarselli; Monica Bianchini; Alessandro Sperduti (ESANN, 2021)

Tangent Graph Convolutional Network. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (ESANN, 2021)

Learn and Visually Explain Deep Fair Models: an Application to Face Recognition. - Danilo Franco; Luca Oneto; Nicolò Navarin; Davide Anguita (IJCNN, 2021)

Simple Multi-resolution Gated GNN. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (SSCI, 2021)

Efficient Multilingual Deep Learning Model for Keyword Categorization. - Mirko Polato; Denys Demchenko; Almat Kuanyshkereyev; Nicolò Navarin (SSCI, 2021)

Simple Graph Convolutional Networks. - Luca Pasa; Nicolò Navarin; Wolfgang Erb; Alessandro Sperduti (CoRR, 2021)

Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition. - Danilo Franco; Luca Oneto; Nicolò Navarin; Davide Anguita (Entropy, 2021)

Deep Learning for Graphs. - Davide Bacciu; Federico Errica; Nicolò Navarin; Luca Pasa; Daniele Zambon (ESANN, 2022)

Biased Edge Dropout in NIFTY for Fair Graph Representation Learning. - Federico Caldart; Luca Pasa; Luca Oneto; Alessandro Sperduti; Nicolò Navarin (ESANN, 2022)

Backpropagation-free Graph Neural Networks. - Luca Pasa; Nicolò Navarin; Wolfgang Erb; Alessandro Sperduti (ICDM, 2022)

Face the Truth: Interpretable Emotion Genuineness Detection. - Matteo Cardaioli; Alessio Miolla; Mauro Conti; Giuseppe Sartori; Merylin Monaro; Cristina Scarpazza; Nicolò Navarin (IJCNN, 2022)

Forged handwriting verification: a public domain dataset for training machine learning models. - Merylin Monaro; Valentina Fietta; Valentina Curró; Giulia Lusetti; Giuseppe Sartori; Nicolò Navarin (IJCNN, 2022)

Understanding Catastrophic Forgetting of Gated Linear Networks in Continual Learning. - Matteo Munari; Luca Pasa; Daniele Zambon; Cesare Alippi; Nicolò Navarin (IJCNN, 2022)

Compact graph neural network models for node classification. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (SAC, 2022)

Detecting deception through facial expressions in a dataset of videotaped interviews: A comparison between human judges and machine learning models. - Merylin Monaro; Stéphanie Maldera; Cristina Scarpazza; Giuseppe Sartori; Nicolò Navarin (Comput. Hum. Behav., 2022)

Deep fair models for complex data: Graphs labeling and explainable face recognition. - Danilo Franco; Nicolò Navarin; Michele Donini; Davide Anguita; Luca Oneto (Neurocomputing, 2022)

Advances in artificial neural networks, machine learning and computational intelligence. - Luca Oneto; Kerstin Bunte; Nicolò Navarin (Neurocomputing, 2022)

Towards learning trustworthily, automatically, and with guarantees on graphs: An overview. - Luca Oneto; Nicolò Navarin; Battista Biggio; Federico Errica; Alessio Micheli; Franco Scarselli; Monica Bianchini; Luca Demetrio; Pietro Bongini; Armando Tacchella; Alessandro Sperduti (Neurocomputing, 2022)

Advances in artificial neural networks, machine learning and computational intelligence. - Luca Oneto; Nicolò Navarin; Frank-Michael Schleif (Neurocomputing, 2022)

Polynomial-based graph convolutional neural networks for graph classification. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (Mach. Learn., 2022)

SOM-based aggregation for graph convolutional neural networks. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (Neural Comput. Appl., 2022)

Editorial Special Issue Interaction With Artificial Intelligence Systems: New Human-Centered Perspectives and Challenges. - Merylin Monaro; Emilia I. Barakova; Nicolò Navarin (IEEE Trans. Hum. Mach. Syst., 2022)

Multiresolution Reservoir Graph Neural Network. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (IEEE Trans. Neural Networks Learn. Syst., 2022)

Graph-based Explainable Recommendation Systems: Are We Rigorously Evaluating Explanations? - Andrea Montagna; Alvise De Biasio; Nicolò Navarin; Fabio Aiolli (HCAI4U@CHItaly, 2023)

Graph Representation Learning. - Davide Bacciu; Federico Errica; Alessio Micheli; Nicolò Navarin; Luca Pasa; Marco Podda; Daniele Zambon (ESANN, 2023)

An Empirical Study of Over-Parameterized Neural Models based on Graph Random Features. - Nicolò Navarin; Luca Pasa; Luca Oneto; Alessandro Sperduti (ESANN, 2023)

Women and Gender Disparities in Computer Science: A Case Study at the University of Padua. - Valentina Fietta; Nicolò Navarin; Merylin Monaro; Ombretta Gaggi (GoodIT, 2023)

An Untrained Neural Model for Fast and Accurate Graph Classification. - Nicolò Navarin; Luca Pasa; Claudio Gallicchio; Alessandro Sperduti (ICANN (4), 2023)

Topology preserving maps as aggregations for Graph Convolutional Neural Networks. - Paolo Frazzetto; Luca Pasa; Nicolò Navarin; Alessandro Sperduti (SAC, 2023)

Object-centric process predictive analytics. - Riccardo Galanti; Massimiliano de Leoni; Nicolò Navarin; Alan Marazzi (Expert Syst. Appl., 2023)

On the problem of recommendation for sensitive users and influential items: Simultaneously maintaining interest and diversity. - Alvise De Biasio; Merylin Monaro; Luca Oneto; Lamberto Ballan; Nicolò Navarin (Knowl. Based Syst., 2023)

An explainable decision support system for predictive process analytics. - Riccardo Galanti; Massimiliano de Leoni; Merylin Monaro; Nicolò Navarin; Alan Marazzi; Brigida Di Stasi; Stéphanie Maldera (Eng. Appl. Artif. Intell., 2023)

A systematic review of value-aware recommender systems. - Alvise De Biasio; Andrea Montagna; Fabio Aiolli; Nicolò Navarin (Expert Syst. Appl., 2023)

Automated Synthesis of Certified Neural Networks. - Matteo Zavatteri; Davide Bresolin; Nicolò Navarin (ECAI, 2024)

Towards the application of Backpropagation-Free Graph Convolutional Networks on Huge Datasets. - Nicolò Navarin; Luca Pasa; Alessandro Sperduti (ESANN, 2024)

Informed Machine Learning for Complex Data. - Luca Oneto; Nicolò Navarin; Alessio Micheli; Luca Pasa; Claudio Gallicchio; Davide Bacciu; Davide Anguita (ESANN, 2024)

Relative Local Signal Strength: The Impact of Normalization on the Analysis of Neuroimaging Data with Deep Learning. - Giovanni Donghi; Luca Pasa; Alberto Testolin; Marco Zorzi; Alessandro Sperduti; Nicolò Navarin (ICANN (8), 2024)

Physics-Informed Graph Neural Cellular Automata: an Application to Compartmental Modelling. - Nicolò Navarin; Paolo Frazzetto; Luca Pasa; Pietro Verzelli; Filippo Visentin; Alessandro Sperduti; Cesare Alippi (IJCNN, 2024)

Automated Synthesis of Certified Neural Networks: Initial Results and Open Research Lines. - Matteo Zavatteri; Davide Bresolin; Nicolò Navarin (OVERLAY, 2024)

Beyond the Additive Nodes' Convolutions: a Study on High-Order Multiplicative Integration. - Paolo Frazzetto; Luca Pasa; Nicolò Navarin; Alessandro Sperduti (SAC, 2024)

Model-based approaches to profit-aware recommendation. - Alvise De Biasio; Dietmar Jannach; Nicolò Navarin (Expert Syst. Appl., 2024)

Investigating over-parameterized randomized graph networks. - Giovanni Donghi; Luca Pasa; Luca Oneto; Claudio Gallicchio; Alessio Micheli; Davide Anguita; Alessandro Sperduti; Nicolò Navarin (Neurocomputing, 2024)

Economic recommender systems - a systematic review. - Alvise De Biasio; Nicolò Navarin; Dietmar Jannach (Electron. Commer. Res. Appl., 2024)

Fair graph representation learning: Empowering NIFTY via Biased Edge Dropout and Fair Attribute Preprocessing. - Danilo Franco; Vincenzo Stefano D'Amato; Luca Pasa; Nicolò Navarin; Luca Oneto (Neurocomputing, 2024)

Advances in artificial neural networks, machine learning and computational intelligence. - Nicolò Navarin; Dounia Mulders; Luca Oneto (Neurocomputing, 2024)

A unified framework for backpropagation-free soft and hard gated graph neural networks. - Luca Pasa; Nicolò Navarin; Wolfgang Erb; Alessandro Sperduti (Knowl. Inf. Syst., 2024)

Empowering Simple Graph Convolutional Networks. - Luca Pasa; Nicolò Navarin; Wolfgang Erb; Alessandro Sperduti (IEEE Trans. Neural Networks Learn. Syst., 2024)

RGCVAE: relational graph conditioned variational autoencoder for molecule design. - Davide Rigoni 001; Nicolò Navarin; Alessandro Sperduti (Mach. Learn., 2025)

Online Continual Graph Learning. - Giovanni Donghi; Luca Pasa; Daniele Zambon; Cesare Alippi; Nicolò Navarin (CoRR, 2025)