5 citations · 9 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
Heterogeneous Sheaf Neural Networks
Luke Braithwaite, Alessio Borgi, Gabriele Onorato +4
Heterogeneous graphs, whose nodes and edges can belong to different types and feature spaces, arise in many real-world domains, including biology, recommendation, social networks,…
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…