1. COMPREHENSIVE TEMPORAL DIFFUSION MODELING WITH CORRELATED TEXT COMPONENT Xie Yiran 2014.5.13

2. CONTENTS Introduction Introduction Related Work Related Work Basic Model Basic Model Joint Model Joint Model Experimental Evaluation Experimental Evaluation

3. CONTENTS Introduction Introduction Related Work Related Work Basic Model Basic Model Joint Model Joint Model Experimental Evaluation Experimental Evaluation

4. INTRODUCTION Lots of messages are published on the Social medias Including Correlated texts

5. CORRELATED TEXTS Re-sharing texts (71%) Re-creating texts (29%)

6. CORRELATED TEXTS Re-sharing texts (78%) Re-creating texts (22%)

7. CORRELATED TEXTS The volume of messages changes with the time goes by The volume of messages changes with the time goes by

8. CORRELATED TEXTS How to model temporal diffusion with the correlated texts? How to model temporal diffusion with the correlated texts? And help prediction /recommendation /ad … on micro-blog platform And help prediction /recommendation /ad … on micro-blog platform

9. CONTENTS Introduction Introduction Related Work Related Work Basic Model Basic Model Joint Model Joint Model Experimental Evaluation Experimental Evaluation

10. RELATED WORK LIM: Modeling information diffusion in implicit network LIM: Modeling information diffusion in implicit network SPIKEM: Rise and fall patterns of information diffusion SPIKEM: Rise and fall patterns of information diffusion SSM: modeling and predicting behavioral dynamics on the web SSM: modeling and predicting behavioral dynamics on the web Analytical model for temporal variation Analytical model for temporal variation Complicated implicit information Complicated implicit information Cannot make good use of correlated texts Cannot make good use of correlated texts Meme-tracker: Meme-tracking and the dynamics of the news cycle Meme-tracker: Meme-tracking and the dynamics of the news cycle Global model for temporal variation Global model for temporal variation Theoretical Theoretical

11. RELATED WORK Most current work Most current work employ the simple collective counting methods employ the simple collective counting methods ignore the essential characteristics of temporal variations ignore the essential characteristics of temporal variations cannot make good use of correlated texts cannot make good use of correlated texts

12. CONTENTS Introduction Introduction Related Work Related Work Basic Model Basic Model Joint Model Joint Model Experimental Evaluation Experimental Evaluation

13. BASIC MODEL Social media : scale-free network Social media : scale-free network Growth : start with m nodes and add new nodes Growth : start with m nodes and add new nodes Preferential attachment : new nodes prefer to attach to big nodes Preferential attachment : new nodes prefer to attach to big nodes ∏(k)~k γ ∏(k)~k γ Initial attractiveness: a new node attaches to a isolated node Initial attractiveness: a new node attaches to a isolated node ∏(k)~(A+k) γ ∏(k)~(A+k) γ Growth constraints: real network has finite lifetime or finite edge capacity Growth constraints: real network has finite lifetime or finite edge capacity Gradual aging : ∏(k)~(A+k) γ *t - β Gradual aging : ∏(k)~(A+k) γ *t - β

14. BASIC MODEL x t ~ (A+x t-1 ) γ *t - β x t ~ (A+x t-1 ) γ *t - β γ ~ 2?γ ~ 2? β ~ 1.2β ~ 1.2

15. JOINT MODEL Growth: start with re-creating nodes, add re-sharing nodes. Growth: start with re-creating nodes, add re-sharing nodes. Re-creating action: x t ~ (A+x t-1 ) γ * B * t - α Re-creating action: x t ~ (A+x t-1 ) γ * B * t - α Re-sharing action: x t ~ (A+x t-1 ) γ *t - β Re-sharing action: x t ~ (A+x t-1 ) γ *t - β

16. JOINT MODEL Periodicity p(t) Periodicity p(t) x t ~ [(A+x t-1 ) γ *(t - α +B*t - β )] * p(t) x t ~ [(A+x t-1 ) γ *(t - α +B*t - β )] * p(t)

17. CONTENTS Introduction Introduction Related Work Related Work Micro-blog Data Characteristics Micro-blog Data Characteristics Basic Model Basic Model Joint Model Joint Model Experimental Evaluation Experimental Evaluation

18. EXPERIMENTAL EVALUATION Matching data Matching data

19. EXPERIMENTAL EVALUATION Matching patterns Matching patterns

20. EXPERIMENTAL EVALUATION Prediction Prediction

21. TO BE CONTINUED Motivation and Meaning Motivation and Meaning Deduction of model Deduction of model Comparison in experiment part Comparison in experiment part