In light of the wide application of Graph Neural Networks (GNNs), Membership Inference Attack (MIA) against GNNs raises severe privacy concerns, where training data can be …
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 …
Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Despite the great success, recent studies have shown that GNNs are still vulnerable to …
Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new family of machine learning (ML) models, have been proposed to fully leverage graph data to …
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 …
Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications. However, recent studies have shown that GNNs are vulnerable to adversarial …
X Wang, WH Wang - Proceedings of the 2022 ACM SIGSAC Conference …, 2022 - dl.acm.org
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 …
M Ju, Y Fan, C Zhang, Y Ye - Proceedings of the AAAI Conference on …, 2023 - ojs.aaai.org
Abstract Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to essential applications requiring solid robustness or vigorous …
Node injection attack on Graph Neural Networks (GNNs) is an emerging and practical attack scenario that the attacker injects malicious nodes rather than modifying original nodes or …