16 citations · 30 across the 8 of their papers we have counts for
17 papers · 1 filter
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…
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…
Phase Transition Adaptation
Claudio Gallicchio, Alessio Micheli, Luca Silvestri
Artificial Recurrent Neural Networks are a powerful information processing abstraction, and Reservoir Computing provides an efficient strategy to build robust implementations by pr…
Pyramidal Reservoir Graph Neural Network
Filippo Maria Bianchi, Claudio Gallicchio, Alessio Micheli
We propose a deep Graph Neural Network (GNN) model that alternates two types of layers. The first type is inspired by Reservoir Computing (RC) and generates new vertex features by…
Graph Mixture Density Networks
Federico Errica, Davide Bacciu, Alessio Micheli
We introduce the Graph Mixture Density Networks, a new family of machine learning models that can fit multimodal output distributions conditioned on graphs of arbitrary topology. B…
Ring Reservoir Neural Networks for Graphs
Claudio Gallicchio, Alessio Micheli
Machine Learning for graphs is nowadays a research topic of consolidated relevance. Common approaches in the field typically resort to complex deep neural network architectures and…