320 citations · 740 across the 21 of their papers we have counts for
27 papers
Modeling Edge Features with Deep Bayesian Graph Networks
Daniele Atzeni, Federico Errica, Davide Bacciu +1
We propose an extension of the Contextual Graph Markov Model, a deep and probabilistic machine learning model for graphs, to model the distribution of edge features. Our approach i…
An Empirical Evaluation of Rewiring Approaches in Graph Neural Networks
Alessio Micheli, Domenico Tortorella
Graph neural networks compute node representations by performing multiple message-passing steps that consist in local aggregations of node features. Having deep models that can lev…
Addressing Heterophily in Node Classification with Graph Echo State Networks
Alessio Micheli, Domenico Tortorella
Node classification tasks on graphs are addressed via fully-trained deep message-passing models that learn a hierarchy of node representations via multiple aggregations of a node's…
Beyond Homophily with Graph Echo State Networks
Domenico Tortorella, Alessio Micheli
Graph Echo State Networks (GESN) have already demonstrated their efficacy and efficiency in graph classification tasks. However, semi-supervised node classification brought out the…
Dynamic Graph Echo State Networks
Domenico Tortorella, Alessio Micheli
Dynamic temporal graphs represent evolving relations between entities, e.g. interactions between social network users or infection spreading. We propose an extension of graph echo…
TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence
Davide Bacciu, Siranush Akarmazyan, Eric Armengaud +32
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment c…