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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.

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WSDM'16 Relational Learning with Social Status Analysis

  • 1. 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.
  • 2. 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
  • 3. 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.
  • 4. 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
  • 5. 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.