Graph embeddings have been proposed to map graph data to low dimensional space for
downstream processing (eg, node classification or link prediction). With the increasing …

Graph is an important data representation ubiquitously existing in the real world. However,
analyzing the graph data is computationally difficult due to its non-Euclidean nature. Graph …

Recently, the surge in popularity of the Internet of Things (IoT), mobile devices, social media,
etc., has opened up a large source for graph data. Graph embedding has been proved …

As machine learning becomes more widely used for critical applications, the need to study
its implications in privacy turns to be urgent. Given access to the target model and auxiliary …

Many data mining tasks rely on graphs to model relational structures among individuals
(nodes). Since relational data are often sensitive, there is an urgent need to evaluate the …

While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in
real-world applications, they have been shown to be vulnerable to a growing number of …

Graph structured data have enabled several successful applications such as
recommendation systems and traffic prediction, given the rich node features and edges …

Many real-world data comes in the form of graphs, such as social networks and protein
structure. To fully utilize the information contained in graph data, a new family of machine …

Recent research has shown that machine learning (ML) models are vulnerable to privacy
attacks that leak information about the training data. In this work, we consider Graph Neural …

Graph Neural Networks (GNNs) have demonstrated superior performance in learning node
representations for various graph inference tasks. However, learning over graph data can …