报告题目：Top-K Similarity Search on Graph Data

　　报告人：李佩

　　报告时间：2012年10月9日星期二晚7点

　　报告地点：信息学院一层会议室

　　报告人简介：

　　李佩，加拿大不列颠哥伦比亚大学计算机系博士生，2007年于华中科技大学计算机系获本科学位，2010年于中国人民大学信息学院获硕士学位。2009年8月至12月在香港中文大学数据库组任研究助理，2010年2月至8月在微软亚洲研究院任研究实习生。从2009年至今在ICDE,SDM, PAKDD等会议发表论文近10篇，曾获得ADMA 2009和SDM2010会议最佳论文奖，微软亚洲研究院明日之星实习生荣誉。主要研究兴趣为图数据管理与挖掘，社交网络搜索与挖掘等等。

　　报告主要内容：
　　Search for objects similar to a given query object in a network has numerous applications including web search and collaborative filtering. We use the notion of structural similarity to capture the commonality of two objects in a network, e.g.,if two nodes are referenced by the same node, they may besimilar. Meeting-based methods including SimRank and P-Rank capture structural similarity very well. Deriving inspiration from PageRank, SimRank has gained popularity by a natural intuition and domain independence. Since it’s computationally expensive,subsequent work has focused on optimizing and approximating the computation of SimRank. In this talk, we introduce an algorithmic framework called TopSim based on transforming the top-k SimRank problem on a graph G to one of finding the top-k nodes with highest  authority on the product graph G *G.

　　欢迎有兴趣的老师和学生参加

                                                                                                                               信息学院
                                                                                                                            2012年10月9日