09-influence_annot

09-influence_annot -...

Info iconThis preview shows pages 1–8. Sign up to view the full content.

View Full Document Right Arrow Icon
CS224W: Social and Information Network Analysis re eskovec tanford University Jure Leskovec, Stanford University http://cs224w.stanford.edu
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
ue ED Oct 20 MIDNIGHT! Due WED Oct 20 MIDNIGHT! Answer the following questions: What is the problem you are solving? What data will you use ( how will you get it )? How will you do the project? Which algorithms/models you plan to develop? Be as specific as you can! Who will you evaluate , measure success? hat do you expect to bmit/accomplish y the end? What do you expect to submit/accomplish by the end? Submission instructions: Upload PDF to http://coursework.stanford.edu Get your GroupdID at http://bit.ly/bZmdGA Name your file: <GroupID>_proposal.pdf
Background image of page 2
[Leskovec et al., SDM ’07] Posts Blogs ime Information cascade Time ordered hyperlinks Data – Blogs: We crawled 45,000 blogs for 1 year 10 million posts and 350,000 cascades Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 10/13/2009 3
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Cascade shapes (ranked by frequency) he probability of The probability of observing a cascade on n nodes follows a ount Zipf distribution: p(n) ~ n -2 Co x = Cascade size (number of nodes) 10/13/2009 4 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu
Background image of page 4
Most of cascades are trees r of edges diameter Numbe Effective d Cascade size (number of nodes) Cascade size Count Number of joined cascades Cascades per node 10/13/2009 5 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
What’s a good model? What role does the underlying social network play? Can make a step towards more realistic cascade generation (propagation) model? Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 10/13/2009 6
Background image of page 6
1) Randomly pick blog to fect add to cascade 2) Infect each in linked eighbor with probability infect, add to cascade. neighbor with probability  B 1 B 2 B 1 B 1 B 1 B 2 B B 3 B 4 B 3 3) Add infected neighbors to cascade. 4) Set node infected in (i) to
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 8
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 01/11/2011 for the course CS 224 at Stanford.

Page1 / 27

09-influence_annot -...

This preview shows document pages 1 - 8. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online