1 citations · 1 across the 6 of their papers we have counts for
6 papers
Subgraph Concept Networks: Concept Levels in Graph Classification
Lucie Charlotte Magister, Alexander Norcliffe, Iulia Duta +1
The reasoning process of Graph Neural Networks is complex and considered opaque, limiting trust in their predictions. To alleviate this issue, prior work has proposed concept-based…
Wasserstein Hypergraph Neural Network
Iulia Duta, Pietro Liò
The ability to model relational information using machine learning has driven advancements across various domains, from medicine to social science. While graph representation learn…
Sheaves Reloaded: A Directional Awakening
Stefano Fiorini, Hakan Aktas, Iulia Duta +4
Sheaf Neural Networks (SNNs) represent a powerful generalization of Graph Neural Networks (GNNs) that significantly improve our ability to model complex relational data. While dire…
Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts
Shiye Su, Iulia Duta, Lucie Charlotte Magister +1
Hypergraph neural networks are a class of powerful models that leverage the message passing paradigm to learn over hypergraphs, a generalization of graphs well-suited to describing…
SPHINX: Structural Prediction using Hypergraph Inference Network
Iulia Duta, Pietro Liò
The importance of higher-order relations is widely recognized in a large number of real-world systems. However, annotating them is a tedious and sometimes impossible task. Conseque…
Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks
Ferran Hernandez Caralt, Guillermo Bernárdez Gil, Iulia Duta +2
Sheaf Neural Networks (SNNs) naturally extend Graph Neural Networks (GNNs) by endowing a cellular sheaf over the graph, equipping nodes and edges with vector spaces and defining li…