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 …

Learning on Graphs has attracted immense attention due to its wide real-world applications.
The most popular pipeline for learning on graphs with textual node attributes primarily relies …

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, many carefully designed graph representation learning methods have achieved
impressive performance on either strong heterophilic or homophilic graphs, but not both …

Graph Neural Networks (GNNs) have become increasingly important due to their
representational power and state-of-the-art predictive performance on many fundamental …

Abstract Graph Neural Networks (GNNs) have garnered significant attention for their
success in learning the representation of homophilic or heterophilic graphs. However, they …

Link prediction is a common task on graph-structured data that has seen applications in a
variety of domains. Classically, hand-crafted heuristics were used for this task. Heuristic …

Graph Neural Networks (GNNs) have achieved great success on a variety of tasks with
graph-structural data, among which node classification is an essential one. Unsupervised …

Graphs are ubiquitous for modeling complex relationships between objects across various
fields. Graph neural networks (GNNs) have become a mainstream technique for graph …

The growing ubiquity of relational data structured as graphs has underscored the need for
graph learning models with exceptional generalization capabilities. However, current …