papers

Here you can find selected publications from the Task Force.

D. Bacciu, D. Numeroso, Explaining Deep Graph Networks via Input Perturbation, IEEE Transactions on Neural Networks and Learning Systems, 2022

FM Bianchi, D Grattarola, L Livi, C Alippi, Graph neural networks with convolutional arma filters, IEEE Transactions on pattern analysis and machine intelligence, Volume: 44, Issue: 7, 2022

D. Castellana, F. Errica, D. Bacciu, A. Micheli, The Infinite Contextual Graph Markov Model, ICML 2022

FM Bianchi, C Gallicchio, A Micheli, Pyramidal Reservoir Graph Neural Network, Neurocomputing 470, 389-404, 2022

D. Bacciu, A. Conte, R. Grossi, F. Landolfi, A. Marino, K-plex cover pooling for graph neural networks, Data Mining and Knowledge Discovery, volume 35, pages 2200–2220, 2021

F. Errica, D. Bacciu, A. Micheli, Graph Mixture Density Networks, ICML 2021, pages 3025-3035, 2021

D. Bacciu, F.Errica, A. Micheli, M. Podda, A Gentle Introduction to Deep Learning for Graphs, Neural Networks, Volume 129, September 2020, Pages 203-221

D. Bacciu, A Micheli, Deep Learning for Graphs. In: Oneto L., Navarin N., Sperduti A., Anguita D. (eds) Recent Trends in Learning From Data. Studies in Computational Intelligence, vol 896. Springer, Cham, 2020

F Errica, M Podda, D Bacciu, A Micheli, A fair comparison of graph neural networks for graph classification, Proceedings of the 8th International Conference on Learning Representations, ICLR, 2020

Z Zhang, P Cui and W Zhu, Deep Learning on Graphs: A Survey, IEEE Transactions on Knowledge and Data Engineering, 2020