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 …

Anomalous node detection in a static graph faces significant challenges due to the rarity of
anomalies and the substantial cost of labeling their deviant structure and attribute patterns …

The propagation of rumours on social media poses an important threat to societies, so that
various techniques for graph-based rumour detection have been proposed recently. Existing …

Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes,
edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its …

Modern recommender systems derive predictions from an interaction graph that links users
and items. To this end, many of today's state-of-the-art systems use graph neural networks …

Label imbalance and homophily-heterophily mixture are the fundamental problems
encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection …

Financial fraud is one of the most significant social issues and has caused tremendous
property losses. Graph neural networks (GNNs) have been applied to anti-fraud practices …

Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised
node binary classification task. In recent years, Graph Neural Networks (GNN) have been …

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 …