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 …
Graph mamba: Towards learning on graphs with state space models
Graph Neural Networks (GNNs) have shown promising potential in graph representation
learning. The majority of GNNs define a local message-passing mechanism, propagating …
learning. The majority of GNNs define a local message-passing mechanism, propagating …
Where did the gap go? reassessing the long-range graph benchmark
The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of
graph learning tasks strongly dependent on long-range interaction between vertices …
graph learning tasks strongly dependent on long-range interaction between vertices …
Spatio-spectral graph neural networks
Abstract Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for
learning on graph-structured data. However, key limitations of ℓ-step MPGNNs are that their" …
learning on graph-structured data. However, key limitations of ℓ-step MPGNNs are that their" …
[PDF][PDF] Current and future directions in network biology
Network biology is an interdisciplinary field bridging computational and biological sciences
that has proved pivotal in advancing the understanding of cellular functions and diseases …
that has proved pivotal in advancing the understanding of cellular functions and diseases …
Cooperative graph neural networks
Graph neural networks are popular architectures for graph machine learning, based on
iterative computation of node representations of an input graph through a series of invariant …
iterative computation of node representations of an input graph through a series of invariant …
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
S Akansha - arxiv preprint arxiv:2308.15568, 2023 - arxiv.org
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 …