Yuqian Lv
Biographic Data
| ID | 10054067 |
|---|---|
| NAME | Yuqian Lv |
| GIVEN NAMES | Yuqian |
| FAMILY NAME | Lv |
| SIGNATURE | LV Y |
| AFFILIATIONS | Zhejiang University of Technology |
| ORCID | 0009-0005-1909-9965 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Single-Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning
Graph neural networks (GNNs) have achieved remarkable success in various real-world applications. However, recent studies highlight the vulnerability of GNNs to malicious perturbations. Previous adversaries primarily focus on graph modifications or node injections to existing graphs, yielding promising results but with notable limitations. Graph modification attack (GMA) requires manipulation of the original graph, which is often impractical, whi…
Attacking the Core Structure of Complex Network
The concept of $k$ -core in complex networks plays a key role in many applications, e.g., understanding the global structure or identifying central/critical nodes, of a network. A malicious attacker with a jamming ability can exploit the vulnerability of the $k$ -core structure to attack the network and invalidate the network analysis methods, e.g., reducing the $k$ -shell values of nodes can deceive graph algorithms, leading to the wrong decisio…
No prominent works on this page.
Attacking the Core Structure of Complex Network
The concept of $k$ -core in complex networks plays a key role in many applications, e.g., understanding the global structure or identifying central/critical nodes, of a network. A malicious attacker with a jamming ability can exploit the vulnerability of the $k$ -core structure to attack the network and invalidate the network analysis methods, e.g., reducing the $k$ -shell values of nodes can deceive graph algorithms, leading to the wrong decisio…
Single-Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning
Graph neural networks (GNNs) have achieved remarkable success in various real-world applications. However, recent studies highlight the vulnerability of GNNs to malicious perturbations. Previous adversaries primarily focus on graph modifications or node injections to existing graphs, yielding promising results but with notable limitations. Graph modification attack (GMA) requires manipulation of the original graph, which is often impractical, whi…
Advanced Graph Neural Networks (2 works) · Computer Science (2 works) · Advanced Computing and Algorithms (1 work) · Algorithm (1 work) · Arithmetic (1 work) · Artificial Intelligence (1 work) · Artificial neural network (1 work) · Brain Tumor Detection and Classification (1 work) · Complex Network Analysis Techniques (1 work) · Computer security (1 work)