As social media dominates our lives in increasing intensity, the need for developers to understand the theory and applications is ongoing as well. This book serves that purpose.

Social network analysis is the solicitation of network science on social networks, and social occurrences are denoted and premeditated by data on coinciding pairs as the entities of opinion.

The book features:

• Social network analysis from a computational perspective using python to show the significance of fundamental facets of network theory and the various metrics used to measure the social network.
• An understanding of network analysis and motivations to model phenomena as networks. 
• Real-world networks established with human-related data frequently display social properties, i.e., patterns in the graph from which human behavioral patterns can be analyzed and extracted.
• Exemplifies information cascades that spread through an underlying social network to achieve widespread adoption.
• Network analysis that offers an appreciation method to health systems and services to illustrate, diagnose, and analyze networks in health systems.
• The social web has developed a significant social and interactive data source that pays exceptional attention to social science and humanities research.
• The benefits of artificial intelligence enable social media platforms to meet an increasing number of users and yield the biggest marketplace, thus helping social networking analysis distribute better customer understanding and aiding marketers to target the right customers.

Audience

The book will interest computer scientists, AI researchers, IT and software engineers, mathematicians.