Graphs are ubiquitous in encoding relational information of real-world objects in many
domains. Graph generation, whose purpose is to generate new graphs from a distribution …

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) are widely used in machine learning for graph-structured
data. Even though GNNs have achieved remarkable success in real-world applications …

The realm of graph-based modeling has proven its adaptability across diverse real-world
data types. However, its applicability to general computer vision tasks had been limited until …

Many real-world datasets can be naturally represented as graphs, spanning a wide range of
domains. However, the increasing complexity and size of graph datasets present significant …

With the increase of multi-view graph data, multi-view graph clustering (MVGC) that can
discover the hidden clusters without label supervision has attracted growing attention from …

Graph anomaly detection is crucial for identifying nodes that deviate from regular behavior
within graphs, benefiting various domains such as fraud detection and social network …

Abstract Graph Neural Networks (GNNs) have been a prevailing technique for tackling
various analysis tasks on graph data. A key premise for the remarkable performance of …

Abstract Graph Neural Networks (GNNs) obtain tremendous success in modeling relational
data. Still, they are prone to adversarial attacks, which are massive threats to applying GNNs …

Graph Neural Networks (GNNs) have shown superior performance in modeling graph data.
Existing studies have shown that a lot of GNNs perform well on homophilic graphs while …