Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Learning representations on large-sized graphs is a long-standing challenge due to the inter-
dependence nature involved in massive data points. Transformers, as an emerging class of …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

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

Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently,
provably boosting the expressive power of standard (message-passing) GNNs. However …

Large-scale graph training is a notoriously challenging problem for graph neural networks
(GNNs). Due to the nature of evolving graph structures into the training process, vanilla …

Graph neural networks (GNNs) have emerged due to their success at modeling graph data.
Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs …

Abstract Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art
method for graph-based learning tasks. However, training GCNs at scale is still challenging …

Abstract Graph Neural Networks (GNNs) have shown exceptional performance in the task of
link prediction. Despite their effectiveness, the high latency brought by non-trivial …

The prevalence of graph-based data has spurred the rapid development of graph neural
networks (GNNs) and related machine learning algorithms. Yet, despite the many datasets …