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Predictive Analytics of Social Networks: A Survey of Tasks and Techniques

Predictive Analytics of Social Networks: A Survey of Tasks and Techniques

Ming Yang, William H. Hsu, Surya Teja Kallumadi
Copyright: © 2016 |Pages: 37
ISBN13: 9781466695627|ISBN10: 1466695625|EISBN13: 9781466695634
DOI: 10.4018/978-1-4666-9562-7.ch056
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MLA

Yang, Ming, et al. "Predictive Analytics of Social Networks: A Survey of Tasks and Techniques." Business Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 1080-1116. https://doi.org/10.4018/978-1-4666-9562-7.ch056

APA

Yang, M., Hsu, W. H., & Kallumadi, S. T. (2016). Predictive Analytics of Social Networks: A Survey of Tasks and Techniques. In I. Management Association (Ed.), Business Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 1080-1116). IGI Global. https://doi.org/10.4018/978-1-4666-9562-7.ch056

Chicago

Yang, Ming, William H. Hsu, and Surya Teja Kallumadi. "Predictive Analytics of Social Networks: A Survey of Tasks and Techniques." In Business Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1080-1116. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9562-7.ch056

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Abstract

In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and phenomena that generated it. They begin by defining five basic tasks that can be performed using social networks: (1) link prediction; (2) pathway and community formation; (3) recommendation and decision support; (4) risk analysis; and (5) planning, especially causal interventional planning. Next, they discuss frameworks for using predictive analytics, availability of annotation, text associated with (or produced within) a social network, information propagation history (e.g., upvotes and shares), trust, and reputation data. They also review challenges such as imbalanced and partial data, concept drift especially as it manifests within social media, and the need for active learning, online learning, and transfer learning. They then discuss general methodologies for predictive analytics involving network topology and dynamics, heterogeneous information network analysis, stochastic simulation, and topic modeling using the abovementioned text corpora. They continue by describing applications such as predicting “who will follow whom?” in a social network, making entity-to-entity recommendations (person-to-person, business-to-business [B2B], consumer-to-business [C2B], or business-to-consumer [B2C]), and analyzing big data (especially transactional data) for Customer Relationship Management (CRM) applications. Finally, the authors examine a few specific recommender systems and systems for interaction discovery, as part of brief case studies.

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