报告题目：When Dense Subgraph Detection Meets Data Mining Tasks
主讲人： 杨禹 香港城市大学
Dense Subgraph Detection is a fundamental task in data science research, and enjoys a lot of important applications ranging from correlation analysis of entities and community detection to real-time story identification. Most existing studies focus on dense subgraph mining itself, while combing dense subgraphs with other important data mining tasks are often overlooked. In this talk, I will introduce our recent research in incorporating dense subgraph detection to two important data mining tasks, contrast data mining and social propagation analysis. We adopt subgraph densities as the measurement to evaluate the effectiveness of the two tasks, and analyze the hardness of seeking for the optimal solution w.r.t. the new measurement. We also devise both efficient and effective algorithms to tackle the new challenges in these two tasks caused by incorporating dense subgraphs. Experimental results on large-scale datasets demonstrate the superiority of our algorithms over the baselines. I will conclude by discussing some future directions related to our research.
Yu Yang is currently an Assistant Professor with the School of Data Science at the City University of Hong Kong. His research interests lie in the algorithmic aspects of data mining and data science, with an emphasis on managing and mining dynamics of large-scale networks. His work appears in premier venues such as SIGMOD, VLDB, ICDE, IJCAI, CIKM, TKDE, TKDD, and KAIS. He obtained his Ph.D. in Computing Science from the Simon Fraser University in Feb. 2019. Before that, he received his B.E. degree from the Hefei University of Technology in 2010, and his M.E. degree from the University of Science and Technology of China in 2013, both in Computer Science.
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