Graph data are pervasive in real-world scenarios, and research on graph data has become
a research hotspot. Over the past few decades, significant advancements have been made …

Graph Neural Networks (GNNs) have emerged as the dominant approach for machine
learning on graph-structured data. However, concerns have arisen regarding the …

Node classification using Graph Neural Networks (GNNs) has been widely applied in
various real-world scenarios. However, in recent years, compelling evidence emerges that …

Temporal Graph Networks (TGNs) have shown remarkable performance in learning
representation for continuous-time dynamic graphs. However, real-world dynamic graphs …

Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in various
graph representation learning tasks. Recently, studies revealed their vulnerability to …

MiRNA has distinct physiological functions at various cellular locations. However, few
effective computational methods for predicting the subcellular location of miRNA exist …

Graph Neural Networks (GNNs), which are nowadays the benchmark approach in graph
representation learning, have been shown to be vulnerable to adversarial attacks, raising …

As a research hotspot for graph mining technology, Graph Convolutional Networks (GCN)
have achieved remarkable performance in the fields of wireless networks, Internet of Things …

Artificial neural networks (ANNs) have achieved extraordinary performance in various
domains in recent years. However, some studies reveal that ANNs may be vulnerable in …

Graphs G=(V, E) can be represented using:• adjacency matrix A∈{0, 1} n× n where Aij= 1iff
(i, j)∈ E.• unnormalised graph Laplacian matrix L= D− A, where D= diag (A1n).• symmetric …