Recently, Transformer model, which has achieved great success in many artificial
intelligence fields, has demonstrated its great potential in modeling graph-structured data …

Graph transformers have emerged as a promising architecture for a variety of graph learning
and representation tasks. Despite their successes, though, it remains challenging to scale …

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 …

The Transformer architecture has achieved remarkable success in a number of domains
including natural language processing and computer vision. However, when it comes to …

The graph Transformer emerges as a new architecture and has shown superior
performance on various graph mining tasks. In this work, we observe that existing graph …

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 …

Graph Neural Networks (GNNs) have shown promising potential in graph representation
learning. The majority of GNNs define a local message-passing mechanism, propagating …

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 …

Abstract Graph Transformers (GTs) have proved their advantage in graph-level tasks.
However, existing GTs still perform unsatisfactorily on the node classification task due to 1) …

Recent studies have highlighted the limitations of message-passing based graph neural
networks (GNNs), eg, limited model expressiveness, over-smoothing, over-squashing, etc …