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 …

Abstract Graph Neural Networks (GNNs) had been demonstrated to be inherently
susceptible to the problems of over-smoothing and over-squashing. These issues prohibit …

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 …

Graph Neural Networks (GNNs) have succeeded in various computer science applications,
yet deep GNNs underperform their shallow counterparts despite deep learning's success in …

Graph Neural Networks (GNNs) excel in various graph learning tasks but face computational
challenges when applied to large-scale graphs. A promising solution is to remove non …

Graph Neural Networks (GNNs) have emerged as a revolutionary paradigm in the realm of
machine learning, offering a transformative approach to dissect intricate relationships …

Graph Neural Networks (GNNs) are the state-of-the-art model for machine learning on graph-
structured data. The most popular class of GNNs operate by exchanging information …

Message-passing graph neural networks (MPNNs) emerged as powerful tools for
processing graph-structured input. However, they operate on a fixed input graph structure …

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 …

Abstract While Graph Neural Networks (GNNs) have been successfully leveraged for
learning on graph-structured data across domains, several potential pitfalls have been …