C - SNAP - Stanford University

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The link prediction task: Given G[t 0,t 0’] a graph on edges up to time t 0’ output a ranked list L of links (not in G[t 0,t 0’]) that are predicted to appear in G[t 1,t 1’] Evaluation: n=|E new|: # new edges that appear during the test period [t 1,t 1’] [LibenNowell-Kleinberg ‘03] Take top n elements of L and count correct edges 12/6/2011 Jure Leskovec, <strong>Stanford</strong> CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 2
Predict links in a evolving collaboration network Core: Since network data is very sparse Consider only nodes with in-degree and out-degree of at least 3 [LibenNowell-Kleinberg ‘03] 12/6/2011 Jure Leskovec, <strong>Stanford</strong> CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 3
Page 1: CS224W: Social and Information Netw
Page 5 and 6: For every pair of nodes (x,y) compu
Page 7 and 8: Improvement over #common neighbors
Page 9 and 10: Recommend a list of possible friend
Page 11 and 12: Let s be the center node Let f w(u
Page 13 and 14: a uv …. Strength of edge (u,v,)
Page 15 and 16: Want to minimize: Loss: h(x)=0 if
Page 17 and 18: How to minimize F? Take the deriva
Page 19 and 20: Facebook Iceland network 174,000 n
Page 21: Facebook: predict future friends A
Page 24 and 25: There is a hidden diffusion network
Page 26 and 27: 12/6/2011 Jure Leskovec, Stanford C
Page 28 and 29: Continuous time cascade diffusion m
Page 30 and 31: Given node infection times and patt
Page 32 and 33: Score of a graph G for a set of cas
Page 34 and 35: Theorem: F c(G) is monotonic, and s
Page 36 and 37: Synthetic data: Take a graph G on
Page 38 and 39: With 2x as many infections as edges
Page 40 and 41: 5,000 news sites: Blogs Mainstream

C - SNAP - Stanford University

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