题　目：Analyzing Bipartite Graphs with Cohesive Structures: Applications, Challenges, and Algorithms

嘉　宾：Kai Wang, Research Fellow, University of New South Wales

主持人：林学民　讲席教授　上海交通大学安泰经济与管理学院

时　间：2022年2月22日（周二）13:30-15:00（腾讯会议）

（校内师生如需会议号和密码，请于2月21日12:00前发送电邮至 dbi@acem.sjtu.edu.cn 获取）

内容简介:

Bipartite graph is a popular data structure, which arises naturally when modeling two different types of entities in many real-world applications. For instance, in E-commerce, the purchasing relationships between users and products can be modeled as a bipartite graph. Cohesive structure, which can be either a motif (i.e., small repeating pattern) or a cohesive subgraph, plays an important role in analyzing bipartite graphs. Due to the special property of bipartite graphs, algorithms for computing cohesive structures in general (unipartite) graphs cannot be easily extended to handle bipartite graphs. To enable efficient and effective bipartite graph analytics with cohesive structures, my recent studies have addressed the key challenges for solving a set of fundamental problems in both motif level (butterfly counting and reachability query) and subgraph level (bi-community search and bitruss decomposition). My work in this area generates significant impacts, evidenced by 12 papers published in flagship conferences/journals including ACM SIGMOD, PVLDB, IEEE ICDE, VLDBJ, and IEEE TKDE, and the high citation number of these papers as well as follow-up works from research groups in world-leading universities (e.g., MIT). In this talk, I will show the applications, challenges, algorithms, and experimental results for each problem.

演讲人简介：

Kai Wang is currently a research fellow in the Data and Knowledge Research Group, School of Computer Science and Engineering of The University of New South Wales. He obtained his Ph.D. from The University of New South Wales in 2020 and his bachelor’s degree from Zhejiang University in 2016, both in Computer Science. His research interests lie in processing, managing, and analyzing big data, especially for complex graph/network and spatial data. His dedication to research has resulted in one book and more than 20 conference/journal papers including 3 currently under revision. Most of his research works were published in top-tier conferences (e.g., ACM SIGMOD, PVLDB, and IEEE ICDE) and journals (e.g., VLDBJ and IEEE TKDE). He serves as a program committee member in many flagship conferences such as ACM SIGKDD 2022, IEEE ICDE 2022, WSDM 2022, WISE 2021/2022, and CIKM 2021. He is the Web Chair of VLDB 2022 (Sydney) and the Publicity Chair of LSGDA 2020 (Tokyo).

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