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 …

Along with the rapid technological and commercial innovation on the e-commerce platforms,
there are an increasing number of frauds that bring great harm to these platforms. Many …

Graph neural networks (GNNs) have demonstrated remarkable success for semisupervised
node classification. However, these GNNs are still limited to the conventionally …

With the great popularity of Graph Neural Networks (GNNs), their robustness to adversarial
topology attacks has received significant attention. Although many attack methods have …

Along with the rapid technological and commercial innovation on e-commerce platforms, an
increasing number of frauds cause great harm to these platforms. Many frauds are …

In this work, we study the problem of partitioning a set of graphs into different groups such
that the graphs in the same group are similar while the graphs in different groups are …

Abstract Graph Neural Networks (GNNs) are powerful tools in graph application areas.
However, recent studies indicate that GNNs are vulnerable to adversarial attacks, which can …

In this paper, we propose a framework that uses the theory and techniques of (Social)
Network Analysis to investigate the learned representations of a Graph Neural Network …

The identification and implementation of high-quality ideas have been hindered in the open
innovation community due to information overload. Most existing studies tended to utilize the …

In recent years, Graph Neural Networks (GNNs) have performed significantly in node
classification tasks. Re-search on GNNs adversarial attacks has recently attracted attention …