Trustworthy and Intelligent Large-Scale Graph Data Management
Date: 2026/01/23 – 2026/01/23
Academic Seminar: Trustworthy and Intelligent Large-Scale Graph Data Management
Speaker: Yizhang He, Associate Lecturer in the School of Computer Science and Engineering at the University of New South Wales (UNSW), Australia
Time: 9:00-10:00 a.m., January 23, 2026 (Beijing Time)
Location: online
Abstract
Large-scale graph data management underpins critical applications in financial risk control, network security, and public governance. However, sensitive graph data, billion-scale graphs, and the need for automated analysis create fundamental challenges in balancing privacy, efficiency, and intelligence.
In this talk, I will present my research on trustworthy and intelligent large-scale graph data management through three integrated directions. First, privacy-preserving graph data management using differential privacy techniques that enable accurate estimation of structural patterns while protecting individual edge information. I developed vertex-priority algorithms for general graphs (SIGMOD 2025) and unbiased estimation methods for bipartite graphs that reduce communication costs by up to 80% (ICDE 2024, SIGMOD 2025).
Second, efficient dense subgraph queries that enable interactive queries on billion-scale graphs, achieving up to two orders of magnitude speedup (SIGMOD 2023), along with GPU-accelerated algorithms for production deployment.
Third, intelligent graph data management through the integration of knowledge graphs and large language models to build natural-language interfaces, and multi-agent systems that automate complex analysis workflows.
My work has been recognized as state-of-the-art, with selected techniques adopted in production by industrial partners such as Alibaba. I will discuss future research directions that integrate privacy protection, scalable algorithms, and intelligent automation to realize a unified framework for trustworthy and intelligent large-scale graph data management.
Biography
Yizhang He is an Associate Lecturer in the School of Computer Science and Engineering at the University of New South Wales (UNSW), Australia. He completed his Ph.D. in Computer Science at UNSW under the supervision of Professors Wenjie Zhang and Xuemin Lin. His research focuses on trustworthy large-scale graph data management, with particular emphasis on differential privacy, efficient query processing, and graph modeling.
Dr. He has published 12 papers in top-tier venues including SIGMOD, ICDE, VLDB Journal, and TKDE, with 4 first-author papers in SIGMOD. His work has been recognized by leading researchers as state-of-the-art, and selected techniques have been adopted in production by industrial partners such as Alibaba. He has extensive teaching experience at UNSW, with student evaluations consistently exceeding School and Faculty averages. Dr. He was selected for the UNSW Early Career Academic Fellowship in 2025.