{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

15-spectral_annot

15-spectral_annot - CS224W: JureLeskovec,StanfordUniversity...

Info iconThis preview shows pages 1–10. Sign up to view the full content.

View Full Document Right Arrow Icon
CS224W: Social and Information Network Analysis Jure Leskovec Stanford University Jure Leskovec, 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 Document Right Arrow Icon
Task: Find coalitions in signed networks Incentives: European chocolates! F Fame Up to 10% extra credit D Due: Friday midnight No late days! 11/8/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 2
Background image of page 2
Today: 3 methods (3) Trawling: Community signatures that can be efficiently extracted (4) S t l h titi i L l i t i 2 d i t (4) Spectral graph partitioning: Laplacian matrix, 2nd eigenvector (5) Overlapping communities: Clique percolation method 11/8/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 3
Background image of page 3

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

View Full Document Right Arrow Icon
[Kumar et al. ‘99] Searching for small communities in Web graph (1) What is the signature of a community/discussion in a Web graph Use this to define topics: Wh t th l th What the same people on the left talk about on the right 11/8/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu A dense 2 layer graph Intuition: many people all talking about the same things 4
Background image of page 4
[Kumar et al. ‘99] (2) A more well defined problem: (2) A more well defined problem: Enumerate complete bipartite subgraphs K s,t Where K s nodes where each links to the s,t = same t other nodes 11/8/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 5
Background image of page 5

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

View Full Document Right Arrow Icon
[Kumar et al. ‘99] Two points: (1) The signature of a community/discussion (2) Complete bipartite subgraph K s,t K s,t = graph on s nodes, each links to the same t other nodes Plan: (A) From (2) get back to (1): Via: Any dense enough graph contains a smaller K s,t as a subgraph (B) How do we solve (2) in a giant graph? What similar problems have been solved on big non graph data? What similar problems have been solved on big non graph data? (3) Frequent itemset enumeration [Agrawal Srikant ‘99] 11/8/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 6
Background image of page 6
[Agrawa Srikant ‘99] Marketbasket analysis: What items are bought together in a store? Setting: Universe U of n items b t f U S S S U m subsets of U : S 1 , S 2 , …, S m ( S i is a set of items one person bought) Frequency threshold f Goal: Fi d ll b t T t T S f f t S Find all subsets s.t. i of f sets i (items in T were bought together f times) 11/8/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 7
Background image of page 7

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

View Full Document Right Arrow Icon