Are defenses for graph neural networks robust?
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 …
effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard …
Dos-gnn: Dual-feature aggregations with over-sampling for class-imbalanced fraud detection on graphs
As fraudulent activities have shot up manifolds, fraud detection has emerged as a pivotal
process in different fields (eg, e-commerce, online reviews, and social networks). Since …
process in different fields (eg, e-commerce, online reviews, and social networks). Since …
Distilling knowledge on text graph for social media attribute inference
The popularization of social media generates a large amount of user-oriented data, where
text data especially attracts researchers and speculators to infer user attributes (eg, age …
text data especially attracts researchers and speculators to infer user attributes (eg, age …
Hover: Homophilic oversampling via edge removal for class-imbalanced bot detection on graphs
As malicious bots reside in a network to disrupt network stability, graph neural networks
(GNNs) have emerged as one of the most popular bot detection methods. However, in most …
(GNNs) have emerged as one of the most popular bot detection methods. However, in most …
Adversarial diffusion attacks on graph-based traffic prediction models
Real-time traffic prediction models play a pivotal role in smart mobility systems and have
been widely used in route guidance, emerging mobility services, and advanced traffic …
been widely used in route guidance, emerging mobility services, and advanced traffic …
Adversary for social good: Leveraging attribute-obfuscating attack to protect user privacy on social networks
As social networks become indispensable for people's daily lives, inference attacks pose
significant threat to users' privacy where attackers can infiltrate users' information and infer …
significant threat to users' privacy where attackers can infiltrate users' information and infer …
Pseudo-Labeling with Graph Active Learning for Few-shot Node Classification
Graphs have emerged as one of the most important and powerful data structures to perform
content analysis in many fields. In this line of work, node classification is a classic task …
content analysis in many fields. In this line of work, node classification is a classic task …
Hierarchical graph neural network for patient treatment preference prediction with external knowledge
The healthcare industry has a wealth of data that can be used by researchers and medical
professionals to infer a patient's condition and intention to receive treatment using machine …
professionals to infer a patient's condition and intention to receive treatment using machine …
Towards defense against adversarial attacks on graph neural networks via calibrated co-training
Graph neural networks (GNNs) have achieved significant success in graph representation
learning. Nevertheless, the recent work indicates that current GNNs are vulnerable to …
learning. Nevertheless, the recent work indicates that current GNNs are vulnerable to …
A comparative study on robust graph neural networks to structural noises
Graph neural networks (GNNs) learn node representations by passing and aggregating
messages between neighboring nodes. GNNs have been applied successfully in several …
messages between neighboring nodes. GNNs have been applied successfully in several …