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08/18/2023
Investigating the Interplay between Features and Structures in Graph Learning
In the past, the dichotomy between homophily and heterophily has inspire...
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08/17/2023
Modeling Edge Features with Deep Bayesian Graph Networks
We propose an extension of the Contextual Graph Markov Model, a deep and...
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05/17/2023
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabili...
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02/24/2022
Bayesian Deep Learning for Graphs
The adaptive processing of structured data is a long-standing research t...
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03/22/2021
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification
In this work, we study the phenomenon of catastrophic forgetting in the ...
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12/05/2020
Graph Mixture Density Networks
We introduce the Graph Mixture Density Network, a new family of machine ...
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07/14/2020
Accelerating the identification of informative reduced representations of proteins with deep learning for graphs
The limits of molecular dynamics (MD) simulations of macromolecules are ...
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06/01/2020
Concept Matching for Low-Resource Classification
We propose a model to tackle classification tasks in the presence of ver...
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01/24/2020
Theoretically Expressive and Edge-aware Graph Learning
We propose a new Graph Neural Network that combines recent advancements ...
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12/29/2019
A Gentle Introduction to Deep Learning for Graphs
The adaptive processing of graph data is a long-standing research topic ...
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12/20/2019
A Fair Comparison of Graph Neural Networks for Graph Classification
Experimental reproducibility and replicability is a critical topic in ma...
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05/27/2018