Graph representation learning on vast datasets, like web data, has made significant strides.
However, the associated computational and storage overheads raise concerns. In sight of …

Graph neural networks (GNNs) tend to suffer from high computation costs due to the
exponentially increasing scale of graph data and a large number of model parameters …

Graph neural networks (GNNs) are emerging for machine learning research on graph-
structured data. GNNs achieve state-of-the-art performance on many tasks, but they face …

Spatial-temporal graph models are prevailing for abstracting and modelling spatial and
temporal dependencies. In this work, we ask the following question: whether and to what …

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 the leading deep learning models for
graph-based representation learning. However, the training and inference of GNNs on large …

Graph Neural Networks have demonstrated remarkable effectiveness in various graph-
based tasks, but their inefficiency in training and inference poses significant challenges for …

Graph Neural Networks (GNNs) have demonstrated superior performance across various
graph learning tasks but face significant computational challenges when applied to large …

Graph neural network (GNN) is a promising method to analyze graphs. Most existing GNNs
adopt the class-balanced assumption, which cannot deal with class-imbalanced graphs …

Due to the significant computational challenge of training large-scale graph neural networks
(GNNs), various sparse learning techniques have been exploited to reduce memory and …