Abstract Graph Neural Networks (GNNs) have received extensive research attention due to
their powerful information aggregation capabilities. Despite the success of GNNs, most of …

Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean
data and have been widely used in various fields. However, most of the existing GNN …

Scalable graph neural networks (GNNs) have emerged as a promising technique, which
exhibits superior predictive performance and high running efficiency across numerous large …

Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph-based
tasks. However, their inefficiency in training and inference presents challenges for scaling …

Facing the increasing heterogeneity of data in the real world, multi-view learning has
become a crucial area of research. Graph Convolutional Networks (GCNs) are powerful for …

Graph Neural Networks (GNNs), known as spectral graph filters, find a wide range of
applications in web networks. To bypass eigendecomposition, polynomial graph filters are …

Heterophilous graphs, where dissimilar nodes tend to connect, pose a challenge for graph
neural networks (GNNs) as their superior performance typically comes from aggregating …

Graphs provide a natural, intuitive, and holistic means to capture relationships between
different text elements in Natural Language Processing (NLP) such as words, sentences …

The paper aims to explore the untapped potential of hypergraphs by leveraging attribute-rich
and multi-layered structures. The primary objective is to develop an innovative learning …

Vulnerable node detection in uncertain graphs is a typical graph mining problem that seeks
to identify nodes at a high risk of breakdown under the joint effect from both the self and …