Probabilistic Approaches for Social Media Analysis: Data, Community and Influence

✍ Scribed by Kun Yue, Jin Li, Hao Wu, Weiyi Liu, Zidu Yin

Publisher: World Scientific
Year: 2020
Tongue: English
Leaves: 290
Series: East China Normal University Scientific Reports 11
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

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

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