Graph data augmentation has been used to improve generalizability of graph machine
learning. However, by only applying fixed augmentation operations on entire graphs …

Edge perturbation is a basic method to modify graph structures. It can be categorized into
two veins based on their effects on the performance of graph neural networks (GNNs), ie …

Graph-level anomaly detection aims at capturing anomalous individual graphs in a graph
set. Due to its significance in various real-world application fields, eg, identifying rare …

Graph convolutional networks (GCNs) can quickly and accurately learn graph
representations and have shown powerful performance in many graph learning domains …

Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown
state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show …

Graph Neural Networks'(GNNs) ability to generalize across complex distributions is crucial
for real-world applications. However, prior research has primarily focused on specific types …

We develop a novel data-driven nonlinear mixup mechanism for graph data augmentation
and present different mixup functions for sample pairs and their labels. Mixup is a data …

Attribute graphs are a crucial data structure for graph communities. However, the presence
of redundancy and noise in the attribute graph can impair the aggregation effect of …

Graph neural networks (GNNs) have achieved state-of-the-art performance in representation
learning for graphs recently. However, the effectiveness of GNNs, which capitalize on the …

Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-
world signed graphs containing positive and negative links. However, three key challenges …