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On over-squashing in message passing neural networks: The impact of width, depth, and topology
Abstract Message Passing Neural Networks (MPNNs) are instances of Graph Neural
Networks that leverage the graph to send messages over the edges. This inductive bias …
Networks that leverage the graph to send messages over the edges. This inductive bias …
Revisiting over-smoothing and over-squashing using ollivier-ricci curvature
Abstract Graph Neural Networks (GNNs) had been demonstrated to be inherently
susceptible to the problems of over-smoothing and over-squashing. These issues prohibit …
susceptible to the problems of over-smoothing and over-squashing. These issues prohibit …
Understanding oversquashing in gnns through the lens of effective resistance
Message passing graph neural networks (GNNs) are a popular learning architectures for
graph-structured data. However, one problem GNNs experience is oversquashing, where a …
graph-structured data. However, one problem GNNs experience is oversquashing, where a …
Expander graph propagation
Deploying graph neural networks (GNNs) on whole-graph classification or regression tasks
is known to be challenging: it often requires computing node features that are mindful of both …
is known to be challenging: it often requires computing node features that are mindful of both …
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
Graph neural networks (GNNs) are able to leverage the structure of graph data by passing
messages along the edges of the graph. While this allows GNNs to learn features …
messages along the edges of the graph. While this allows GNNs to learn features …
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 …
typically follow the message-passing paradigm, whereby the feature of a node is updated …
Probabilistic graph rewiring via virtual nodes
Message-passing graph neural networks (MPNNs) have emerged as a powerful paradigm
for graph-based machine learning. Despite their effectiveness, MPNNs face challenges such …
for graph-based machine learning. Despite their effectiveness, MPNNs face challenges such …
A neural collapse perspective on feature evolution in graph neural networks
Graph neural networks (GNNs) have become increasingly popular for classification tasks on
graph-structured data. Yet, the interplay between graph topology and feature evolution in …
graph-structured data. Yet, the interplay between graph topology and feature evolution in …
Over-squashing in graph neural networks: A comprehensive survey
Graph Neural Networks (GNNs) revolutionize machine learning for graph-structured data,
effectively capturing complex relationships. They disseminate information through …
effectively capturing complex relationships. They disseminate information through …
Rewiring Techniques to Mitigate Oversquashing and Oversmoothing in GNNs: A Survey
Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data,
but their effectiveness is often constrained by two critical challenges: oversquashing, where …
but their effectiveness is often constrained by two critical challenges: oversquashing, where …