The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

A cursory reading of the literature suggests that we have made a lot of progress in designing
effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard …

Graph neural networks (GNNs) are models that capture the dependencies between graph
data by passing messages between graph nodes and they have been widely used to …

Drug–target interaction (DTI) prediction plays an important role in drug repositioning, drug
discovery and drug design. However, due to the large size of the chemical and genomic …

Accessible rapid COVID-19 testing continues to be necessary and several studies involving
deep neural network (DNN) methods for detection have been published. As part of a …

Adversarial attacks have attracted much attention in recent years, and a number of works
have demonstrated the effectiveness of attacks on the entire image at perturbation …

Collecting training data from untrusted sources exposes machine learning services to
poisoning adversaries, who maliciously manipulate training data to degrade the model …

Graph Neural Networks (GNNs) have been extensively used in various real-world
applications. However, the predictive uncertainty of GNNs stemming from diverse sources …

Graph neural networks (GNNs) have enabled the automation of many web applications that
entail node classification on graphs, such as scam detection in social media and event …

Graph Neural Networks (GNNs) are prone to adversarial attacks and discriminatory biases.
The cutting-edge studies usually adopt a perturbation-invariant consistency regularization …