Mohammad Gouse Galety – författare

SOCIAL NETWORK ANALYSIS 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.

This book provides a structured, practical exploration of modern computer networking concepts and the transformative role of artificial intelligence in cybersecurity. This book begins by introducing the foundational principles of computer networks, networking models, protocols, and architectures, together with essential concepts of artificial intelligence. Building on these fundamentals, it examines the evolving cyber threat landscape and explains how AI-driven techniques enhance threat detection, prevention, and response against contemporary attacks, such as phishing, ransomware, and zero-day exploits. It also covers secure networking practices, containerization, automated configuration management, and the role of automation in building resilient infrastructures.Subsequent chapters focus on advanced and applied topics, including Infrastructure as Code, DevSecOps integration, ethical hacking, penetration testing, incident response, and AI-assisted threat hunting. Emerging areas such as zero-trust networking, IoT security, large-scale enterprise deployments, and future trends in AI-powered cybersecurity are also discussed. Emphasis is placed on hands-on learning through labs, case studies, and real-world scenarios, supported by an accompanying GitHub repository.Designed for students and educators, this book bridges theoretical understanding with practical skills required for modern, AI-driven cyber defense environments. Professionals, network administrators, and cybersecurity practitioners interested in AI-driven security and modern network will also find this book useful as a reference.