Many widely used datasets for graph machine learning tasks have generally been
homophilous, where nodes with similar labels connect to each other. Recently, new Graph …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model
in broad application fields for their effectiveness in learning over graphs. To scale GNN …

State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the
graph and model sizes. On large graphs, increasing the model depth often means …

Graph Convolutional Networks (GCNs) are powerful models for learning representations of
attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Graph convolutional networks (GCNs) have received significant attention from various
research fields due to the excellent performance in learning graph representations. Although …

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art deep learning
model for representation learning on graphs. It is challenging to accelerate training of GCNs …

Graph convolutional networks (GCNs) have revolutionized many big data applications, such
as recommendation systems, traffic prediction, etc. However, accelerating GCN inference is …

Graph Neural Networks (GNNs) are proven to be powerful models to generate node
embedding for downstream applications. However, due to the high computation complexity …