Bipartite Graph Analytics: Current Techniques and Future Trends

报告题目: Bipartite Graph Analytics: Current Techniques and Future Trends

报告人:Wenjie Zhang

时间:3月28日下午 14:00 -16:00


Abstract:As the field of data science continues to evolve, bipartite graphs have emerged as a fundamental structure in numerous applications, drawing significant interest from both academic and industrial communities. Bipartite graphs are a specific type of graph consisting of two distinct sets of vertices, where connections only occur between vertices of different sets. Examples include e-commerce networks and biological networks. Analytics of bipartite graphs has become an important research topic in the era of big data. This talk aims to shed light on analysis methods for bipartite graphs, categorizing them into three areas: classical models, learning-based models, and application-driven models. We start by outlining the importance of bipartite graph analytics, and the unique challenges that need to be addressed. Then, we conduct a thorough review of existing works on bipartite graph analytics. We also compare and analyze the models and solutions in these works.

Speaker: Wenjie Zhang is a Professor and ARC Future Fellow in School of Computer Science and Engineering, the University of New South Wales, Australia. She publishes extensively in top venues in database area such as SIGMOD, VLDB, ICDE, PODS, TODS, VLDBJ, and TKDE. Her papers were nominated as Best of SIGMOD and ICDE, and receive Best Paper Awards from DASFAA, WISE, APWeb and ADC. She received Chris Wallace Award from Australasian Computing Research and Education (CORE) in 2019. She serves as an Associate Editor for TKDE and VLDB Journal, and area chair for ICDE/VLDB/ICDM.


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