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 …
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 …
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 …
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 …
An inclusive analysis for performance and efficiency of graph neural network models for node classification
S Ratna, S Singh, A Sharma - Computer Science Review, 2025 - Elsevier
Abstract Graph Neural Networks (GNNs) have become a prominent technique for the
analysis of graph-based data and knowledge extraction. This data can be either structured …
analysis of graph-based data and knowledge extraction. This data can be either structured …
Graph positional and structural encoder
Positional and structural encodings (PSE) enable better identifiability of nodes within a
graph, rendering them essential tools for empowering modern GNNs, and in particular graph …
graph, rendering them essential tools for empowering modern GNNs, and in particular graph …
Probabilistically rewired message-passing neural networks
Message-passing graph neural networks (MPNNs) emerged as powerful tools for
processing graph-structured input. However, they operate on a fixed input graph structure …
processing graph-structured input. However, they operate on a fixed input graph structure …
Recurrent Distance Filtering for Graph Representation Learning
Graph neural networks based on iterative one-hop message passing have been shown to
struggle in harnessing the information from distant nodes effectively. Conversely, graph …
struggle in harnessing the information from distant nodes effectively. Conversely, graph …
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 …