This document proposes a meta-path based approach to predict anchor links between heterogeneous social networks. It introduces connector and recursive meta-paths to model different social factors that influence users joining a new network, such as the number of friends and intimacy between friends in the target network. An experiment on Twitter and Foursquare networks shows the proposed approach outperforms other methods in predicting which Twitter users will join Foursquare, demonstrating the effectiveness of leveraging heterogeneous information from multiple meta-paths.

1. Predicting Anchor Links between
Heterogeneous Social Networks
Sina Sajadmanesh
Hamid R. Rabiee
Ali Khodadadi
Digital Media Lab
Sharif University of Technology
August 2016
2016 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining (ASONAM)

2. Emergence of New Social Networks
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3. New networks try to invite users of older ones
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4. New networks try to invite users of older ones
Invitations can be more intelligent! 4/26

5. Networks connect to each other
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6. Networks connect to each other
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7. Some information is shared between networks
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8. Networks can use information from each other
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9. Problem: Anchor Link Prediction
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10. Problem: Anchor Link Prediction
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11. Why do users join a new network?
Attractiveness of the
target network
Dissatisfaction from the
source network
Number of friends in the
target network
Intimacy between friends
in the target network
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12. Why do users join a new network?
Attractiveness of the
target network
Dissatisfaction from the
source network
Number of friends in the
target network
Intimacy between friends
in the target network
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13. Meta-Path-Based Approach
Network Schema:
• Meta structure of a heterogeneous network
Meta-Path:
• A path defined over network schema
Heterogeneous information
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14. Number of friends in the target network
?
Will he join?
#instances of this meta-path = #friends in the target network
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15. Connector Meta-Paths
?
Will he join? 15/26

16. How to model similarity?
Social
Spatial
Temporal
Textual
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Similarity meta-paths

17. Intimacy between friends in the target network
?
“similarity”
as a measure of
“intimacy”
Will he join? 17/26

18. Recursive Meta-Paths
?
Will he join?
Similarity Extension
again
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19. Classification
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Classification
9 connector
meta-paths
9 features
Path-Count
9x9x9 recursive
meta-paths
729 features
Path-Count

20. Dataset
Twitter as source network:
• Containing about 5k users with a total of 8m tweets
Foursquare as target network:
• Containing about 3.5k users with a total of 48k tips
Common users:
• About 3k shared users
Ground truth:
• Positive Samples: Common users who joined twitter before foursquare
• Negative Samples: Non-anchor users 20/26

21. Experiment Settings
Comparison methods:
• CICF: consistent incidence co-factorization
• CMP: connector meta-paths only
• RMP: recursive meta-paths only
• CRMP: both connector and recursive meta-paths
Experiment setup:
• 1936 positive samples
• 1941 negative samples
• 5-fold cross-validation with linear SVM 21/26

22. Experiment Results
Effect of heterogeneous information
Accuracy AUC
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23. Experiment Results
Effect of remaining anchor links
Accuracy AUC
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24. Experiment Results
Effect of newness of target network
Accuracy AUC
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25. Experiment Results
Effect of similarity extension
Accuracy AUC
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26. Conclusion
Problem:
• Anchor link prediction
• Different from conventional link prediction
Method:
• A meta-path-based approach
• Connector and Recursive meta-paths model different
aspects of social factors
Future Works:
• To model personal factors as well
• To predict the time of link creation
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27. Thank You!
Any Questions?
sajadmanesh@ce.sharif.edu
rabiee@sharif.edu
khodadadi@ce.sharif.edu