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 such as …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks,
are receiving extensive attention for their powerful capability in learning node …

Graph transformers have been competitive on graph classification tasks, but they fail to
outperform Graph Neural Networks (GNNs) on node classification, which is a common task …

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

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 …