Chih-Ya Shen
Biographic Data
| ID | 5152697 |
|---|---|
| NAME | Chih-Ya Shen |
| GIVEN NAMES | Chih-Ya |
| FAMILY NAME | Shen |
| SIGNATURE | SHEN C |
| AFFILIATIONS | National Tsing Hua University |
| ORCID | 0000-0002-0377-7945 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Misinformation Detection via LLM-Based Expert Discussion Network
Link dissolution outperforms link formation in promoting cooperation in social dilemmas
Diversity-Optimized Group Extraction in Social Networks
In this article, we propose to study a novel research problem to boost group performance, that is, social-aware diversity-optimized group extraction (SDGE), which takes into consideration the two important factors: 1) group diversity and 2) social tightness. We prove the NP-hardness of SDGE and propose an effective algorithm, named group shrinking for diversity maximization (GSDM) with a performance guarantee, that is, GSDM is a three-approximati…
Learning to Augment Graphs
This article proposes a machine learning (ML)-based approach to solve a graph optimization problem, named network intervention with limited degradation (NILD), which aims at adding new edges to augment the graph to minimize the local clustering coefficient (LCC) of a target node. The main application of NILD is to perform network intervention , to improve the mental well-being of individuals. This article proposes a new framework, named network i…
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…
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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…
Diversity-Optimized Group Extraction in Social Networks
In this article, we propose to study a novel research problem to boost group performance, that is, social-aware diversity-optimized group extraction (SDGE), which takes into consideration the two important factors: 1) group diversity and 2) social tightness. We prove the NP-hardness of SDGE and propose an effective algorithm, named group shrinking for diversity maximization (GSDM) with a performance guarantee, that is, GSDM is a three-approximati…
Learning to Augment Graphs
This article proposes a machine learning (ML)-based approach to solve a graph optimization problem, named network intervention with limited degradation (NILD), which aims at adding new edges to augment the graph to minimize the local clustering coefficient (LCC) of a target node. The main application of NILD is to perform network intervention , to improve the mental well-being of individuals. This article proposes a new framework, named network i…
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…
Misinformation Detection via LLM-Based Expert Discussion Network
Link dissolution outperforms link formation in promoting cooperation in social dilemmas
Artificial Intelligence (4 works) · Computer Science (4 works) · Graph (3 works) · Machine learning (3 works) · Theoretical Computer Science (3 works) · Advanced Graph Neural Networks (2 works) · Complex Network Analysis Techniques (2 works) · Embedding (2 works) · Expert finding and Q&A systems (2 works) · Mathematics (2 works)