Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …

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

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 …

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

Graph Transformers, emerging as a new architecture for graph representation learning,
suffer from the quadratic complexity and can only handle graphs with at most thousands of …

Abstract Graph Transformers (GTs) have achieved impressive results on various graph-
related tasks. However, the huge computational cost of GTs hinders their deployment and …

Graph neural networks (GNNs) are powerful models for processing graph data and have
demonstrated state-of-the-art performance on many downstream tasks. However, existing …

Graph Transformers (GTs) have demonstrated their advantages across a wide range of
tasks. However, the self-attention mechanism in GTs overlooks the graph's inductive biases …

Graph neural networks are typically trained on individual datasets, often requiring highly
specialized models and extensive hyperparameter tuning. This dataset-specific approach …

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 …