Episode Details

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.

Uploaded 18-08-11

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 and 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 and 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

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