Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Graphs are structured data that models complex relations between real-world entities.
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …

Graph transformers are a recent advancement in machine learning, offering a new class of
neural network models for graph-structured data. The synergy between transformers and …

Scalable graph neural networks (GNNs) have emerged as a promising technique, which
exhibits superior predictive performance and high running efficiency across numerous large …

Graph domain adaptation models have become instrumental in addressing cross-network
learning problems due to their ability to transfer abundant label and structural knowledge …

In the era of smart manufacturing and advanced industrial systems, the high degree of
integration and intelligence of equipment demands higher reliability and safety from …

Recently, the emerging graph Transformers have made significant advancements for node
classification on graphs. In most graph Transformers, a crucial step involves transforming the …

Fake news is pervasive on social media, inflicting substantial harm on public discourse and
societal well-being. We investigate the explicit structural information and textual features of …

While tokenized graph Transformers have demonstrated strong performance in node
classification tasks, their reliance on a limited subset of nodes with high similarity scores for …

Graph transformers need strong inductive biases to derive meaningful attention scores. Yet,
current proposals rarely address methods capturing longer ranges, hierarchical structures …