1. Exploit of Online Social Networks with Community-Based Graph Semi-Supervised Learning
Mingzhen Mo and Irwin King
Department of Computer Science and Engineering,
The Chinese University of Hong Kong

2. Problem Hidden Information ? Some Technique Users’ Profile Friendship
Group & Network
Friendship
Users’
Profile
Some Technique
ICONIP 2010, Sydney

3. Objective
Build a model with proper algorithm to predict the unknown information
Better utilize community information
Previous Works
Graph Theory [G. Flake et al, SIGKDD 2000]
Supervised Learning [E. Zheleva et al, WWW2009]
Semi-Supervised Laerning [M. Mo et al, IJCNN2010]
ICONIP 2010, Sydney

4. Contributions A novel model and algorithms are proposed
Help to understand the security level in social networks.
Community Consistency
Community-Based Graph (CG) SSL
Model 3
Local Consistency
Global Consistency
Basic Graph-Based SSL with Harmonic Function
Local and Global Consistency (LGC) Graph SSL
Model 1
Model 2
ICONIP 2010, Sydney

5. Model Preparation Online Social Network: G(V, E)
Two Sets: labeled data &
unlabeled data P3 P3
W1,3
W3,5 Y1 Y5 P1 P1
W3,4 P5 P5
W1,2
W4,5 P2 P2 P4 P4
W2,4
ICONIP 2010, Sydney

6. Local & Global Consistency (LGC) Learning
Model
Objective
is the Laplace Matrix of community info .
, and
Local & Global Consistency (LGC) Learning
Community Term
Improve…..
ICONIP 2010, Sydney

7. Model Generating Clustering vertices
“Distance” is measured by Group and Network info.
Mark down each cluster in a matrix
_ , nc is the number of clusters
ICONIP 2010, Sydney

8. Model Algorithms Closed form algorithm Simple and time-saving
Input
Process
Output
ICONIP 2010, Sydney

9. Model Algorithms Iterative algorithm
Able to deal with large-scale data
Input
Process
False
True
Output
ICONIP 2010, Sydney

10. Experiments Datasets Objectives Evaluation
One synthetic dataset: TwoMoons
Two real-world datasets: StudiVZ & Facebook
Objectives
classification in TwoMoons
Predict university names in StudiVZ & Facebook
Evaluation
Accuracy and Confidence
ICONIP 2010, Sydney

11. Experiments
Datasets
Improve…..
ICONIP 2010, Sydney

12. Experiments Results – Accuracy of Prediction (a) TwoMoons (b) StudiVZ
(c) Facebook
ICONIP 2010, Sydney

13. Experiments CG SSL performs best in most cases
The curves of CG SSL are not monotone increasing
The accuracies on Facebook dataset are less than the others
ICONIP 2010, Sydney

14. Conclusion
Community-based Graph SSL model describes the real world exactly
CG SSL predicts the hidden information of online social networks with higher accuracy and confidence
The security of users’ information is no longer secure
ICONIP 2010, Sydney

15. Thank you
Q & A
ICONIP 2010, Sydney