Xiujuan Wang

Papers

Total Citations

H-Index

About

Xiujuan Wang is a leading researcher in cybersecurity and social network analysis, with a focused expertise in detecting malicious social robots—automated accounts that spread disinformation and threaten online information integrity. Her work addresses a critical challenge in modern digital environments, where such bots manipulate public opinion and compromise network security. Wang’s major contributions include pioneering the use of generative adversarial networks (GANs) for social robot detection, as demonstrated in her 2019 paper “Detecting Malicious Social Robots with Generative Adversarial Networks” (7 citations), which introduced a novel classification-based approach to identify these threats. She further advanced the field with her 2020 study “Semi-GSGCN: Social Robot Detection Research with Graph Neural Network” (6 citations), leveraging graph neural networks to improve the efficiency and reliability of supervised classification methods. Though her citation counts are modest, reflecting the niche and emerging nature of her research area, Wang’s work is foundational for developing robust defenses against information manipulation. Her innovative integration of deep learning techniques positions her as a key contributor to safeguarding social media ecosystems, making her research essential for students and scholars tackling cybersecurity and AI-driven detection challenges.