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Predictive Models in Social Network Analysis

Predictive Models in Social Network Analysis. 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. Background. Predictive Models To predict in a formal and systematic way.

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Predictive Models in Social Network Analysis

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  1. Predictive Models in SocialNetwork Analysis Zi Yang Tsinghua University Joint work with Prof. Jie Tang, Prof. Juanzi Li, Dr. KekeCai, JingyiGuo, Chi Wang, etc. July 21, 2011, CASIN 2011, Tsinghua

  2. Background • Predictive Models • To predict in a formal and systematic way

  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 Easy to formulate complicated objective function. Easy to derive algorithm based on belief propagation. Easy to deploy on parallel or distributed system.

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

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

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

  7. Message Forwarding Prediction • Problem Definition In Social network （1）Will user uiretweet tweet mjafter reading it? (yij) （2）The spread range of a new m initiated by user u. Tweets Out In

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

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

  10. Social Context Summarization • Modeling Local factor Doc: Tweet： Cross： Similarity > threshold & Dependency factor

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

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