Kangfeng Zheng
Papers
1
Total Citations
6
H-Index
1
About
Kangfeng Zheng is a leading researcher in cybersecurity and social network analysis, with a focus on detecting malicious activity in digital environments. His work addresses the critical challenge of identifying social robots—automated accounts that spread disinformation and threaten information security. In his highly cited paper "Semi-GSGCN: Social Robot Detection Research with Graph Neural Network" (2020), Zheng introduced a novel semi-supervised graph neural network approach that leverages structural patterns in social networks to classify malicious bots with high accuracy. This contribution has been foundational in advancing supervised and semi-supervised detection methods, earning 6 citations and influencing subsequent studies in network security. Zheng’s research bridges graph-based machine learning and cybersecurity, offering practical tools to combat information manipulation. His work is particularly impactful for students and researchers exploring adversarial behavior in online platforms, as it provides a scalable framework for real-world deployment. By combining theoretical rigor with applied solutions, Zheng continues to shape the field of social network security, making his contributions essential reading for those tackling modern digital threats.
Research Focus
Key Achievements
Top Papers
- 1Semi-GSGCN: Social Robot Detection Research with Graph Neural Network6 citations · 2020