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)
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Predicting Anchor Links between Heterogeneous Social Networks
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Predicting Anchor Links between
Heterogeneous Social Networks
Sina SajadmaneshHamid R. RabieeAli Khodadadi
Digital Media Lab
Sharif University of Technology
August 2016
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Emergence of New Social Networks
2/26
New networks try to invite users of older ones
3/26
New networks try to invite users of older ones
Invitations can be more intelligent! 4/26
Networks connect to each other
5/26
Networks connect to each other
6/26
Some information is shared between networks
7/26
Networks can use information from each other
8/26
Problem: Anchor Link Prediction
9/26
Problem: Anchor Link Prediction
10/26
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
11/26
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
12/26
Meta-Path-Based Approach
Network Schema:
• Meta structure of a heterogeneous network
Meta-Path:
• A path defined over network schema
Heterogeneous information
13/26
Number of friends in the target network
?
Will he join?
#instances of this meta-path = #friends in the target network
14/26
Connector Meta-Paths
?
Will he join? 15/26
How to model similarity?
Social
SpatialTemporalTextual
16/26
Similarity meta-paths
Intimacy between friends in the target network
?
“similarity”as a measure of “intimacy”
Will he join? 17/26
Recursive Meta-Paths
?
Will he join?
Similarity Extensionagain
18/26
Classification
19/26
Classification
9 connector meta-paths
9 features
Path-Count
9x9x9 recursive meta-paths
729 features
Path-Count
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
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
Experiment Results
Effect of heterogeneous information
Accuracy AUC22/26
Experiment Results
Effect of remaining anchor links
Accuracy AUC23/26
Experiment Results
Effect of newness of target network
Accuracy AUC24/26
Experiment Results
Effect of similarity extension
Accuracy AUC25/26
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 factorsFuture Works:
• To model personal factors as well• To predict the time of link creation