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Found 5 papers, 2 papers with code
Locality-Aware Graph-Rewiring in GNNs
Graph Neural Networks (GNNs) are popular models for machine learning on graphs that typically follow the message-passing paradigm, whereby the feature of a node is updated recursively upon aggregating information over its neighbors.
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Our analysis provides a unified framework to study different recent methods introduced to cope with over-squashing and serves as a justification for a class of methods that fall under graph rewiring.
Latent Graph Inference using Product Manifolds
The original dDGM architecture used the Euclidean plane to encode latent features based on which the latent graphs were generated.
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression
We demonstrate the applicability of model-agnostic algorithms for meta-learning, specifically Reptile, to GNN models in molecular regression tasks.
Sheaf Neural Networks with Connection Laplacians
A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) that operates on a sheaf, an object that equips a graph with vector spaces over its nodes and edges and linear maps between these spaces.
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