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Dynamic Supervised Community-Topic Model for Enhanced Community Detection and Analysis

This paper introduces a novel Supervised Community-Topic Model that integrates supervised mechanisms into existing community analysis frameworks, specifically ACT models. The model focuses on dynamic evaluation of communities at the topic level, improving community detection quality, establishing citation relationships, and ranking authors more efficiently. Key objectives include detecting communities from topic-level data, conducting dynamic community analysis, and enhancing citation relationships through topic labeling. The proposed model employs Gibbs EM sampling methods for parameter optimization and evaluation metrics, ensuring robust algorithm performance.

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Dynamic Supervised Community-Topic Model for Enhanced Community Detection and Analysis

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  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!

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