1. Dynamic Supervised Community-Topic Model
Daifeng Li

2. Existing Study
1. H Zhang, B Qiu and etc (2007). LDA based Community Structure

3. Existing Study
2. Y Liu, A Niculescu-Mizil and etc(2009). Joint Model of Topic&Author Community (mainly used to make link prediction)

4. Existing Study
3. D Zhou & E Manavoglu(2006). Prob Model for discoverying e-communities. WWW2006

5. The Proposed Model
Integrate Supervised mechanism into ACT model:

6. The Proposed Model The Purpose of New Model:
1. Detect Community from Topic-Level;
2. Make Dynamic analysis on Community and Topic evaluation;
3. Add Topic Label for citation relationship;
4. Improve the quality of detected Community(Topic similarity and conductance)
5. Rank the authors/users in a community in a more efficient way(not only judge by their activities)

7. The Proposed Model The Description of New Model:
1. Supervised Community-Topic Model (Gibbs EM)
Sampling()//Sampling parts belong to E step
{for each document d:
for each author x:
select word w, community cm, topic z, conference c according to prior parameters (multinomial distribution);
end
select supervised author y (whose paper the author x cite as reference) for each assignment according to prior parameters(Gaussian Distribution with mean value as and variance as ); }

8. The Proposed Model The Description of New Model:
1. Supervised Community-Topic Model (Gibbs EM)
How to sample supervised author:
a. Calculate Scores for each supervised author:
Assume the author has written m papers, each paper has been cited for n times, the author’s position in each paper in r, then the score can be computed as:
Score(author x)=
b. Sampling:
For each supervised author, it should be Score*P; where P means
the Prob that author appears in current Community and Topic.

9. The Proposed Model The Description of New Model:
1. Supervised Community-Topic Model (Gibbs EM)
Updating_Parameters() //Optimal Parameters and by M steps {
Update }
Where X is a D*CM matrix, each element is:

10. The Proposed Model The Description of New Model:
1. Supervised Community-Topic Model (Gibbs EM)

11. The Proposed Model The Description of New Model:
for each iterations (1,000):
//E Steps:
for each document d:
for each author x:
Sample comm, topic, word, conference;
end
Sample supervised author for current author;
End
//M Steps:
Update Parameters and

12. The proposed Model
The Description of New Model:

13. Evaluation 1. Average H-Index and Average activities:
For each Community(Compared the paper in WWW 2006);
Compare Conductance between proposed algorithm and paper in WWW 2006;
3. Make prediction for computer committee;
4. Granger/ARMA analysis for Dynamic Community, Topic evolution.

14. Thanks!