關(guān)于舉辦“Top-K Similarity Search on Graph Data”學(xué)術(shù)報(bào)告會(huì)通知
報(bào)告題目：Top-K Similarity Search on Graph Data
報(bào)告人：李佩
報(bào)告時(shí)間：2012年10月9日星期二晚7點(diǎn)
報(bào)告地點(diǎn)：信息學(xué)院一層會(huì)議室
報(bào)告人簡(jiǎn)介：
李佩，加拿大不列顛哥倫比亞大學(xué)計(jì)算機(jī)系博士生，2007年于華中科技大學(xué)計(jì)算機(jī)系獲本科學(xué)位，2010年于中國(guó)人民大學(xué)信息學(xué)院獲碩士學(xué)位。2009年8月至12月在香港中文大學(xué)數(shù)據(jù)庫(kù)組任研究助理，2010年2月至8月在微軟亞洲研究院任研究實(shí)習(xí)生。從2009年至今在ICDE,SDM, PAKDD等會(huì)議發(fā)表論文近10篇，曾獲得ADMA 2009和SDM2010會(huì)議最佳論文獎(jiǎng)，微軟亞洲研究院明日之星實(shí)習(xí)生榮譽(yù)。主要研究興趣為圖數(shù)據(jù)管理與挖掘，社交網(wǎng)絡(luò)搜索與挖掘等等。
報(bào)告主要內(nèi)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.
歡迎有興趣的老師和學(xué)生參加
信息學(xué)院
2012年10月9日