Dynamic Supervised Community-Topic Model

1. Dynamic Supervised Community-Topic Model Daifeng Li 2011-5-10

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 ); end }

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, thenthe 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 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 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); . 2. 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!