Big Data Analytics: A Social Network Approach

Book Description Social networking has increased drastically in recent years, resulting in an increased amount of data being created daily. Furthermore, diversity of issues and complexity of the social networks pose a challenge in social network mining. Traditional algorithm software cannot deal with such complex and vast amounts of data, necessitating the development of novel analytic approaches and tools.
This reference work deals with social network aspects of big data analytics. It covers theory, practices and challenges in social networking. The book spans numerous disciplines like neural networking, deep learning, artificial intelligence, visualization, e-learning in higher education, e-healthcare, security and intrusion detection.

Content

1. Linkage-based Social Network Analysis in Distributed Platform
2. An Analysis of AI-based Supervised Classifiers for Intrusion Detection in Big Data
3. Big Data Techniques in Social Network Analysis
4. Analysis of Deep Learning Tools and Applications in e-Healthcare
5. Assessing the Effectiveness of IPTEACES e-Learning Framework in Higher Education: An Australian’s Perspective
6. Review Study: CAPTCHA Mechanisms for Protecting Web Services against Automated Bots
7. Colored Petri Net Model for Blocking Misleading Information Propagation in Online Social Networks
8. The Conceptual and Architectural Design of Genetic-Based Optimal Pattern Warehousing System
9. Controlling Social Information Cascade: A Survey
10. Swarm-based Analysis for Community Detection in Complex Networks
11. Trustworthiness in the Social Internet of Things
12. Big Data Analysis Technology and Applications
13. Social Networking in Higher Education: Saudi Arabia Perspective
14. Animal Social Networks Analysis: Reviews, Tools and Challenges