Mining Online Data Across Social Networks скачать в хорошем качестве

Mining Online Data Across Social Networks

Capturing Data, Modeling Patterns, Predicting Behavior. Capturing Data, Modeling Patterns, Predicting Behavior - Based on collecting more than 20 million blog posts and news media articles per day, Professor Jure Leskovec discusses how to mine such data to capture and model temporal patterns in the news over a daily time-scale --in particular, the succession of story lines that evolve and compete for attention. He discusses models to quantify the influence of individual media sites on the popularity of news stories and algorithms for inferring hidden networks of information flow. Learn more: http://scpd.stanford.edu/ 0:00 Introduction 0:08 Meet The Speaker 4:01 Many data is a Network! 5:27 Networks: Rich Social Data 6:27 Networks: Size *matters 7:38 Networks: Structure & Process 9:02 Why study Web and networks? 10:28 Projects: Link Prediction 17:17 Projects: Friends vs. Foes 18:13 Projects: Predicting Enemies 20:26 Theory of Structural Balance 22:25 Theory of Status 24:18 Friends vs. Foes, Trust vs. Distrust 27:55 Online (social) media 29:50 Tracking Information on the Web 32:29 How is news being made? 34:35 Modeling influence 36:41 Influence curves of media types 38:35 Inferring the Diffusion Network 39:26 Inferring networks 42:21 Maximizing the Influence 43:35 Influential blogs & Information outbreaks 44:29 Reflections 46:03 Directions 47:23 References 47:34 Autumn Quarter 2011-12 49:09 Graduate Portfolio 50:04 Course Enrollment 51:06 Please indicate your level of interest in the Mining Massive Datasets graduate certificate 54:22 Stanford Center for Professional Development