supervised random walks: predicting and recommending links in social networks lars backstrom...
DESCRIPTION
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 challengeTRANSCRIPT
![Page 1: Supervised Random Walks: Predicting and Recommending Links in Social Networks Lars Backstrom (Facebook) & Jure Leskovec (Stanford) Proc. of WSDM 2011 Present](https://reader035.vdocuments.us/reader035/viewer/2022062504/5a4d1b4f7f8b9ab0599a7071/html5/thumbnails/1.jpg)
Supervised Random Walks: Predicting and Recommending Links
in Social Networks
Lars Backstrom (Facebook) & Jure Leskovec (Stanford)Proc. of WSDM 2011
Present by Mo Mingzhen
![Page 2: Supervised Random Walks: Predicting and Recommending Links in Social Networks Lars Backstrom (Facebook) & Jure Leskovec (Stanford) Proc. of WSDM 2011 Present](https://reader035.vdocuments.us/reader035/viewer/2022062504/5a4d1b4f7f8b9ab0599a7071/html5/thumbnails/2.jpg)
Problem
• Friendship is important on social networks
• How to predict the future interaction
• How to recommend potential friends to new user?
Link Prediction Problem
![Page 3: Supervised Random Walks: Predicting and Recommending Links in Social Networks Lars Backstrom (Facebook) & Jure Leskovec (Stanford) Proc. of WSDM 2011 Present](https://reader035.vdocuments.us/reader035/viewer/2022062504/5a4d1b4f7f8b9ab0599a7071/html5/thumbnails/3.jpg)
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
![Page 4: Supervised Random Walks: Predicting and Recommending Links in Social Networks Lars Backstrom (Facebook) & Jure Leskovec (Stanford) Proc. of WSDM 2011 Present](https://reader035.vdocuments.us/reader035/viewer/2022062504/5a4d1b4f7f8b9ab0599a7071/html5/thumbnails/4.jpg)
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
![Page 5: Supervised Random Walks: Predicting and Recommending Links in Social Networks Lars Backstrom (Facebook) & Jure Leskovec (Stanford) Proc. of WSDM 2011 Present](https://reader035.vdocuments.us/reader035/viewer/2022062504/5a4d1b4f7f8b9ab0599a7071/html5/thumbnails/5.jpg)
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)
![Page 6: Supervised Random Walks: Predicting and Recommending Links in Social Networks Lars Backstrom (Facebook) & Jure Leskovec (Stanford) Proc. of WSDM 2011 Present](https://reader035.vdocuments.us/reader035/viewer/2022062504/5a4d1b4f7f8b9ab0599a7071/html5/thumbnails/6.jpg)
Optimization
• p is the vector of PageRank scores• A “soft” version
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Algorithm
![Page 8: Supervised Random Walks: Predicting and Recommending Links in Social Networks Lars Backstrom (Facebook) & Jure Leskovec (Stanford) Proc. of WSDM 2011 Present](https://reader035.vdocuments.us/reader035/viewer/2022062504/5a4d1b4f7f8b9ab0599a7071/html5/thumbnails/8.jpg)
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 meansrandom guessing.
![Page 9: Supervised Random Walks: Predicting and Recommending Links in Social Networks Lars Backstrom (Facebook) & Jure Leskovec (Stanford) Proc. of WSDM 2011 Present](https://reader035.vdocuments.us/reader035/viewer/2022062504/5a4d1b4f7f8b9ab0599a7071/html5/thumbnails/9.jpg)
Experiments on Real Data
• Four co-authorship networks and the Facebook network of Iceland
• Strength func.
![Page 10: Supervised Random Walks: Predicting and Recommending Links in Social Networks Lars Backstrom (Facebook) & Jure Leskovec (Stanford) Proc. of WSDM 2011 Present](https://reader035.vdocuments.us/reader035/viewer/2022062504/5a4d1b4f7f8b9ab0599a7071/html5/thumbnails/10.jpg)
Interaction Procedure
• The method basically converges in only about 25 iterations
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Results
LR: logistic regression, Prec@20: precision at top 20
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Methods Comparison
• some unsupervised baselines & two supervised learning methods
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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