Graph neural networks (GNNs) demonstrate outstanding performance in a broad range of
applications. While the majority of GNN applications assume that a graph structure is given …

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 …

Graphs are widely used to describe real-world objects and their interactions. Graph Neural
Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly …

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 …

Abstract Graph Neural Networks (GNNs) have emerged as the de facto standard for
representation learning on graphs, owing to their ability to effectively integrate graph …

Graph Neural Networks (GNNs) have been a powerful tool to acquire high-quality node
representations dealing with graphs, which strongly depends on a promising graph …

In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative
and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph …

Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural
networks (GNNs), has shown great potential in boosting the performance of GNNs. Most …

Existing graph neural networks (GNNs) are mostly applied to representation scenes with
complete graph structure. However, the graph structures of complex systems from the real …

Graph neural networks (GNNs) have garnered significant attention for their ability to
effectively process graph-related data. Most existing methods assume that the input graph is …