Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …

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 …

Homophily principle, ie, nodes with the same labels are more likely to be connected, has
been believed to be the main reason for the performance superiority of Graph Neural …

Graph neural networks (GNNs) have recently become the standard approach for learning
with graph-structured data. Prior work has shed light into their potential, but also their …

Graph homophily refers to the phenomenon that connected nodes tend to share similar
characteristics. Understanding this concept and its related metrics is crucial for designing …

Self-supervised space-time correspondence learning utilizing unlabeled videos holds great
potential in computer vision. Most existing methods rely on contrastive learning with mining …

Over the past decade, Graph Neural Networks (GNNs) have achieved great success on
machine learning tasks with relational data. However, recent studies have found that …

Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be
connected to nodes with the same label, challenging the homophily assumption of classical …

Graph homophily refers to the phenomenon that connected nodes tend to share similar
characteristics. Understanding this concept and its related metrics is crucial for designing …

In the current Internet landscape, the rampant spread of fake news, particularly in the form of
multi-modal content, poses a great social threat. While automatic multi-modal fake news …