Jhao-Yin Jhang
Biographic Data
| ID | 10054635 |
|---|---|
| NAME | Jhao-Yin Jhang |
| GIVEN NAMES | Jhao-Yin |
| FAMILY NAME | Jhang |
| SIGNATURE | JHANG J |
| AFFILIATIONS | National Tsing Hua University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Detecting Targets of Graph Adversarial Attacks With Edge and Feature Perturbations
Graph neural networks (GNNs) enable many novel applications and achieve excellent performance. However, their performance may be significantly degraded by the graph adversarial attacks, which intentionally add small perturbations to the graph. Previous countermeasures usually handle such attacks by enhancing model robustness. However, robust models cannot identify the target nodes of the adversarial attacks, and thus we are unable to pinpoint the…
Learning to Extract Expert Teams in Social Networks
Finding a set of suitable experts with minimized communication overhead to perform a complex task finds a wide spectrum of applications in industry, education, and other scenarios. This class of problems, widely formulated as forming a team of experts in social networks (i.e., team formation problem), is very challenging due to its NP-hardness and has attracted much research attention. Although various effective and elegant algorithms have been p…
No prominent works on this page.
Learning to Extract Expert Teams in Social Networks
Finding a set of suitable experts with minimized communication overhead to perform a complex task finds a wide spectrum of applications in industry, education, and other scenarios. This class of problems, widely formulated as forming a team of experts in social networks (i.e., team formation problem), is very challenging due to its NP-hardness and has attracted much research attention. Although various effective and elegant algorithms have been p…
Detecting Targets of Graph Adversarial Attacks With Edge and Feature Perturbations
Graph neural networks (GNNs) enable many novel applications and achieve excellent performance. However, their performance may be significantly degraded by the graph adversarial attacks, which intentionally add small perturbations to the graph. Previous countermeasures usually handle such attacks by enhancing model robustness. However, robust models cannot identify the target nodes of the adversarial attacks, and thus we are unable to pinpoint the…
Artificial Intelligence (2 works) · Computer Science (2 works) · Graph (2 works) · Theoretical Computer Science (2 works) · Advanced Graph Neural Networks (1 work) · Adversarial Robustness in Machine Learning (1 work) · Adversarial system (1 work) · Combinatorics (1 work) · Complex Network Analysis Techniques (1 work) · Computer security (1 work)