Recently, graph neural networks (GNNs) have been widely used for graph representation
learning, where the central idea is to recursively aggregate neighborhood information to …

Abstract Graph Neural Networks (GNNs) are widely used for analyzing graph-structured
data. Most GNN methods are highly sensitive to the quality of graph structures and usually …

Graph Neural Networks (GNNs) are emerging machine learning models on graphs.
Although sufficiently deep GNNs are shown theoretically capable of fully preserving graph …

Abstract Graph Neural Networks (GNNs) have emerged as a useful paradigm to process
graph-structured data. Usually, GNNs are stacked to multiple layers and node …

Graph neural networks (GNNs) have emerged as a powerful tool in graph representation
learning. However, they are increasingly challenged by over-smoothing as network depth …

Graph neural networks (GNNs) have become increasingly popular in modeling graph-
structured data due to their ability to learn node representations by aggregating local …

Abstract Graph Structure Learning (GSL) has recently garnered considerable attention due
to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the …

Graph Neural Network (GNN) is capable of applying deep neural networks to graph
domains. Recently, Message Passing Neural Networks (MPNNs) have been proposed to …

In recent years, graph neural networks (GNNs) have emerged as a successful tool in a
variety of graph-related applications. However, the performance of GNNs can be …

Abstract Graph Neural Networks (GNNs) extend the success of neural networks to graph-
structured data by accounting for their intrinsic geometry. While extensive research has been …