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Supervised Random Walks: Predicting and Recommending Links in Social Networks

Supervised Random Walks: Predicting and Recommending Links in Social Networks. Lars Backstrom (Facebook) & Jure Leskovec (Stanford) Proc. of WSDM 2011 Present by Mo Mingzhen. Problem. Friendship is important on social networks How to predict the future interaction

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Supervised Random Walks: Predicting and Recommending Links in Social Networks

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  1. Supervised Random Walks: Predicting and Recommending Linksin Social Networks Lars Backstrom (Facebook) & Jure Leskovec (Stanford) Proc. of WSDM 2011 Present by Mo Mingzhen

  2. Problem • Friendship is important on social networks • How to predict the future interaction • How to recommend potential friends to new user? Link Prediction Problem

  3. Motivation • Predicting future interaction brings direct business consequences: possible collaborations • Beyond social networks: predicting coauthor/collaborations • In link prediction problem, how to combine the node and edge attributes remains an open challenge

  4. Method • Based on the Supervised Random Walks • Combines the network structure with the characteristics of nodes and edges • Develop an algorithm to estimate the edge strength • bias a PageRank-like random walk to visits given nodes more often

  5. Problem Formulation • Given G(V, E) • A start point s, learning candidate C = {ci} • Destination nodes D = {d1,…,dk}, no-link nodes L = {l1,…,ln}, C = D ∪ L • For edge (u, v) we compute the strength auv = fw(ψuv)

  6. Optimization • p is the vector of PageRank scores • A “soft” version

  7. Algorithm

  8. Experiments on Synthetic Data • A scale-free graph G with 10,000 nodes • Evaluated by classification accuracy • Strength func. *AUC – Area under the ROC curve. 1.0 means perfect classification and 0.5 means random guessing.

  9. Experiments on Real Data • Four co-authorship networks and the Facebook network of Iceland • Strength func.

  10. Interaction Procedure • The method basically converges in only about 25 iterations

  11. Results LR: logistic regression, Prec@20: precision at top 20

  12. Methods Comparison • some unsupervised baselines & two supervised learning methods

  13. Conclusion • The Supervised Random Walks has great improvement over Random Walks. • It outperforms supervised machine learning techniques • It combines rich node and edge features with the structure of the network • Apply to: recommendations, anomaly detection, missing link, and expertise search and ranking

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