Relational Learning with Social Status Analysis 1
Arizona State University
Data Mining and Machine Learning Lab
Relational Learning with
Social Status Analysis
Liang Wu1, Xia Hu2, and Huan Liu1
Arizona State University1 Texas A&M University2
Acknowledgements
This work is sponsored by Office of Naval Research.

Relational Learning with Social Status Analysis 2
Arizona State University
Data Mining and Machine Learning Lab
Inferring User Labels in Online Social Networks
• Infer user labels with user-generated content.
Users Content Labels
0
1
1
Training
Gender
Interests
Age
• How can we estimate user importance?
Model

Relational Learning with Social Status Analysis 3
Arizona State University
Data Mining and Machine Learning Lab
Inferring User Importance with Network Structures
Possible Solution:
– Links are available between
social network users.
– Estimate social status with
Link Analysis?
a
bc
d
Community A
Community BCommunity C
e
Problems:
– Can network centrality represent user importance?
• Information loss
• Redundancy
Solutions:
– Constrain the overall influence of large groups.

Relational Learning with Social Status Analysis 4
Arizona State University
Data Mining and Machine Learning Lab
RESA: Relational Learning with Social Status Analysis
Proposed Approach:
– Introduce group exclusiveness sparsity regularization.
– Effect: Intra-group competition.
For each group
For each member in a group
Individual status
a
bc
d
Community A
Community BCommunity C
Training
Labels
e
Social networks and media content
Weighted Regression

Relational Learning with Social Status Analysis 5
Arizona State University
Data Mining and Machine Learning Lab
Liang Wu
http://www.public.asu.edu/~liangwu1/
wuliang@asu.edu
RESA: Relational Learning with Social Status Analysis
Conclusions:
– Reveal social status from online social networks.
– Incorporate social status to facilitate relational learning.