Machine Learning/Kaggle Social Network Contest/lit review

This page contains links to relevant articles and summaries of the papers.

Papers

Supervised Random Walks

• title: "Supervised Random Walks: Predicting and Recommending Links in Social Networks"
• authors: Lars Backstrom and Jure Leskovec
• paper
• Summary
  • develop an algorithm based on Supervised Random Walks
  • uses network structure info combined with node and edge level attributes to guide the walk
  • learn a function to weight edges s.t. random walker more likely to visit nodes to which new links will be created (equivalent to missing nodes for our application)
  • they develop a good training algorithm.
  • test it on a facebook network and on co-author network
  • compare to decision trees, logistic regression and unsupervised techniques.