C - SNAP - Stanford University

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Node and Edge features for learning: Node: Age, Gender, Degree Edge: Age of an edge, Communication, Profile visits, Co-tagged photos Baselines: Decision trees and logistic regression: Above features + 10 network features (PageRank, common friends) Evaluation: AUC and Precision at Top20 12/6/2011 Jure Leskovec, <strong>Stanford</strong> CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 20
Facebook: predict future friends Adamic-Adar already works great Logistic regression also strong SRW gives slight improvement 12/6/2011 Jure Leskovec, <strong>Stanford</strong> CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 21
Page 1 and 2: CS224W: Social and Information Netw
Page 3 and 4: Predict links in a evolving collabo
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: Facebook Iceland network 174,000 n
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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