Graph Neural Networks (GNNs), which generalize traditional deep neural networks on graph data, have achieved state-of-the-art performance on several graph analytical tasks …
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 …
M Conti, J Li, S Picek, J Xu - Proceedings of the 15th ACM Workshop on …, 2022 - dl.acm.org
Graph Neural Networks (GNNs), inspired by Convolutional Neural Networks (CNNs), aggregate the message of nodes' neighbors and structure information to acquire expressive …
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 …
Graph neural network (GNN), the mainstream method to learn on graph data, is vulnerable to graph evasion attacks, where an attacker slightly perturbing the graph structure can fool …
J Mu, B Wang, Q Li, K Sun, M Xu, Z Liu - Proceedings of the 2021 ACM …, 2021 - dl.acm.org
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph structure related tasks such as node classification and graph classification. However, GNNs …
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 …
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 …
M Zhang, L Hu, C Shi, X Wang - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
With the great popularity of Graph Neural Networks (GNNs), the robustness of GNNs to adversarial attacks has received increasing attention. However, existing works neglect …