Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Hierarchical adversarial attacks against graph-neural-network-based IoT network intrusion detection system
The advancement of Internet of Things (IoT) technologies leads to a wide penetration and
large-scale deployment of IoT systems across an entire city or even country. While IoT …
large-scale deployment of IoT systems across an entire city or even country. While IoT …
Adversarial attack and defense on graph data: A survey
Deep neural networks (DNNs) have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …
image classification, text generation, audio recognition, and graph data analysis. However …
State of the art on adversarial attacks and defenses in graphs
Z Zhai, P Li, S Feng - Neural Computing and Applications, 2023 - Springer
Graph neural networks (GNNs) had shown excellent performance in complex graph data
modelings such as node classification, link prediction and graph classification. However …
modelings such as node classification, link prediction and graph classification. However …
Adversarial label-flipping attack and defense for graph neural networks
With the great popularity of Graph Neural Networks (GNNs), the robustness of GNNs to
adversarial attacks has received increasing attention. However, existing works neglect …
adversarial attacks has received increasing attention. However, existing works neglect …
A hard label black-box adversarial attack against graph neural networks
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 …
structure related tasks such as node classification and graph classification. However, GNNs …
Turning strengths into weaknesses: A certified robustness inspired attack framework against graph neural networks
Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-
related tasks such as node classification. However, recent studies show that GNNs are …
related tasks such as node classification. However, recent studies show that GNNs are …
Adversarial attacks on deep graph matching
Despite achieving remarkable performance, deep graph learning models, such as node
classification and network embedding, suffer from harassment caused by small adversarial …
classification and network embedding, suffer from harassment caused by small adversarial …
How does heterophily impact the robustness of graph neural networks? theoretical connections and practical implications
We bridge two research directions on graph neural networks (GNNs), by formalizing the
relation between heterophily of node labels (ie, connected nodes tend to have dissimilar …
relation between heterophily of node labels (ie, connected nodes tend to have dissimilar …
Adversarial attack against cross-lingual knowledge graph alignment
Recent literatures have shown that knowledge graph (KG) learning models are highly
vulnerable to adversarial attacks. However, there is still a paucity of vulnerability analyses of …
vulnerable to adversarial attacks. However, there is still a paucity of vulnerability analyses of …