Probabilistic Approaches for Social Media Analysis: Data, Community and Influence (East China Normal University Scientific Reports)

"This unique compendium focuses on the acquisition and analysis of social media data. The approaches concern both the data-intensive characteristics and graphical structures of social media. The book addresses the critical problems in social media analysis, which representatively cover its lifecycle. The must-have volume is an excellent reference text for professionals, researchers, academics and graduate students in AI and databases"-- Provided by publisher East China Normal University Scientific Reports Preface About the Authors Acknowledgments Contents 1. Introduction 2. Adaptive and Parallel Acquisition of Social Media Data from Online Big Graphs 3. A Bayesian Network-Based Approach for Incremental Learning of Uncertain Knowledge 4. Discovering User Similarities in Social Behavioral Interactions Based on Bayesian Network 5. Associative Categorization of Frequent Patterns in Social Media Based on Markov Network 6. Markov Network Based Latent Link Discovery and Community Detection in Social Behavioral Interactions 7. Probabilistic Inferences of Latent Entity Associations in Textual Web Contents 8. Containment of Competitive Influence Spread on Social Networks 9. Locating Sources in Online Social Networks via Random Walk 10. Conclusion Index "This unique compendium focuses on the acquisition and analysis of social media data. The approaches concern both the data-intensive characteristics and graphical structures of social media. The book addresses the critical problems in social media analysis, which representatively cover its lifecycle. The must-have volume is an excellent reference text for professionals, researchers, academics and graduate students in AI and databases"--Publisher's website