1. 1 Zi Yang Tsinghua University Joint work with Prof. Jie Tang, Prof. Juanzi Li, Dr. Keke Cai, Jingyi Guo, Chi Wang, etc. July 21, 2011, CASIN 2011, Tsinghua Predictive Models in Social Network Analysis

2. 2 Background Predictive Models –To predict in a formal and systematic way Formulate the predictive objective with a function, and design algorithm for it. Disadvantage: Hard to build objective and derive algorithm. Conventional Build models based on designed parameters and factors. Predict based on belief propagation/sampling-based methods. PGM-based

3. 3 Background Factor graph model –Factor graph A factorized function expressed by a bipartite graph, Node: Parameter node and factor node, Edge: Factor associating parameters. –Factor graph model Belief propagation: message passing along edges. Advantages 1.Easy to formulate complicated objective function. 2.Easy to derive algorithm based on belief propagation. 3.Easy to deploy on parallel or distributed system. Advantages 1.Easy to formulate complicated objective function. 2.Easy to derive algorithm based on belief propagation. 3.Easy to deploy on parallel or distributed system.

4. 4 Outline Predictive Models in Social Network Analysis Example Problems Representative User Finding Message Forwarding Prediction Social Context Summarization preference/relation; unsupervised behavior; supervised IR/NLP; supervised supervision take advantages of conventional textual and social information

5. 5 Representative User Finding Problem Definition Social network In Relationship strength In For user, a representative user Out

6. 6 Representative User Finding Modeling –Edge factor –Regional factor Same representative Different representatives Social homophily Global constraint Avoid “leader without followers”

7. 7 Message Forwarding Prediction Problem Definition Social network In Tweets In

8. 8 Message Forwarding Prediction Modeling –Local factor –Path constraint factor Collaborative decision with his/her followers and followees Sum-of- square error

9. 9 Social Context Summarization Problem Definition Social Context Augmented Network In Doc Social Context (tweets and users) Relationships between sentences, users, tweets Important sentences/tweets Out

10. 10 Social Context Summarization Modeling Local factor Dependency factor Similarity > threshold Similarity > threshold

11. 11 Experiments Data Sets –Arnetminer, Twitter, Digg, News Websites (CNN/BBC/ESPN/MTV), Social Blog (Mashable). Baseline methods –Unsupervised methods, logistic regression, CRF, SVM, etc. Results show improvements over the baseline methods on those applications. –Details are omitted due to time limitation.

12. 12 Thanks a lot! Zi Yang Tsinghua University July 21, 2011, CASIN 2011, Tsinghua