👤 Personal Information

• Class of 2025, Master candidate in Library and Information Science
• Document Analysis and Pattern Recognition Laboratory, School of Information Science and Technology, Shihezi University

🔬 Research Interests

• Complex Network Community Detection

This research explores the evolutionary laws of complex networks over time. Adopting temporal smoothness and network embedding frameworks, it smooths subtle changes of nodes and edges in networks over time to capture inherent evolutionary trends and latent structures. The approach can reveal evolutionary mechanisms hidden behind data, provides new perspectives for understanding complex systems, and shows broad potential for practical applications.

Keywords: dynamic community detection, network embedding, temporal smoothness framework

📄 Academic Outputs

Journal Articles

• Temporal Community Detection and Analysis with Network Embeddings Advances in Trustworthy and Robust Artificial Intelligence, 2025

  This paper proposes TCDA‑NE, a novel TCD algorithm that combines evolutionary clustering with convex non‑negative matrix factorization (Convex‑NMF). Download Paper

Conference Papers

• DCNMF: Dynamic Community Discovery with Improved Convex‑NMF in Temporal Networks International Conference on Collaborative Computing: Networking, Applications and Worksharing, 2022

  This paper presents DCNMF, a dynamic community discovery method using an improved Convex‑NMF approach in temporal networks. Download Paper

• Temporal Smoothness Framework: Analyzing and Exploring Evolutionary Transition Behavior in Dynamic Networks 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), 2021

  This paper presents a temporal smoothness framework to analyze and explore the evolutionary transition behavior in dynamic networks. Download Paper

🏆 Honors & Awards

• Excellent Graduation Project, School of Information Science and Technology, Shihezi University