Books

The future of Artificial Intelligence is connected.From social networks and recommendation engines to autonomous vehicles and cybersecurity systems, modern AI increasingly relies on understanding relationships rather than isolated data points.How do Graph Neural Networks learn from complex networks?How do recommendation systems predict user preferences?How can AI detect fraud, misinformation, and cyber threats using graph structures?How will future Graph Foundation Models transform machine intelligence?Graph Theory with AI Applications: Foundations, Algorithms, and Modern Neural Approaches (VOL-2) provides a comprehensive guide to the technologies driving the next generation of AI.Explore Graph Neural Networks, graph embeddings, knowledge graphs, explainable AI, distributed graph learning, and cutting-edge research topics that are reshaping artificial intelligence.Whether you are a student, researcher, educator, or AI professional, this book will help you understand how intelligent systems learn from relationships, networks, and connected data.Learn the science behind Graph AI. Build the intelligence behind tomorrow's connected world.

Graphs are everywhere.From social media networks and recommendation systems to autonomous vehicles, cybersecurity platforms, and modern artificial intelligence, graph structures have become the language of connected data.But how do machines understand relationships?How do search engines rank billions of pages?How do recommendation systems predict what users will like next?How do AI systems learn from complex networks?Graph Theory with AI Applications: Foundations, Algorithms, and Modern Neural Approaches (VOL-1) provides the answers.This book takes readers on a structured journey through graph fundamentals, graph algorithms, shortest path methods, network optimization, social network analytics, community detection, and graph mining techniques. Designed for students, researchers, educators, and professionals, it combines mathematical foundations with practical AI applications.If you want to understand the science behind connected intelligence and prepare yourself for the future of Graph Neural Networks and AI-driven graph learning, this book is your starting point.Discover the foundations. Master the algorithms. Build the future of Graph AI.

Discover the logical heart of the NFSI. This paper explains the 3-stage pipeline used to transform heterogeneous OSINT signals into a traceable and auditable geopolitical stability index. Check the live data on https://nationfiles.com

A deep dive into the NationFiles Stability Index (NFSI). Discover how 115+ real-time indicators and the Naciro Intelligence Engine redefine geopolitical risk analysis through transparent, rule-based 15-minute recalibration. Check the live data on https://nationfiles.com

Learn LangGraph the fast, practical way by building real projects from your very first chapter. This hands-on guide cuts out the fluff and walks you step-by-step from simple graphs to powerful AI agents like ReAct and RAG. If you prefer learning by doing, this book will get you up and running quickly without overwhelming you.

Move beyond prompts and learn the design patterns behind modern AI agents.From ReAct and RAG to planning, verification, memory, orchestration, and multi-agent collaboration, LLM Agent Patterns shows you how to build LLM systems that are not only powerful, but structured, reliable, and production-minded. If you want to understand how agentic systems actually work—and how to build them yourself—this book is your guide.

"How Does Computer Science Work?" answers 28 key questions every software engineer should know, unraveling the technologies that shape our digital world. From how browsers work and cryptography secures data to AI, Web3, and quantum computing, this book bridges curiosity and understanding. Perfect for students, self-taught programmers, and seasoned developers, it’s your ultimate guide to mastering foundational concepts and cutting-edge innovations in computer science.

Designed for JavaScript developers, this handbook dives into the practical implementation of advanced data structures such as B-Trees, Tries, and probabilistic models like Bloom Filters. Focused on real-world challenges, it explains how to handle large datasets, optimize caching, and implement efficient searches. With clear examples and technical depth, this book offers a realistic approach to applying advanced concepts in scalable and reliable systems.

This is the Elite Insights for 2026 edition.Never know which blockchain or crypto project to focus on or invest in? With so many projects out there, where do you start?! This guide helps you to make an educated decision, not a decision on a whim because someone said: "Hey, you gotta get in to this cool new crypto, it's pumping!" This book covers many blockchains both technically and comparatively, describing Bitcoin, Ethereum, Kaspa, Kadena, Algorand, Saito, Pulsechain, Solana, Sei, Bittensor and Nano. This book also asks the tough questions unveiling interesting insights:Does the Blockchain Trilemma really exist? Is the Trilemma a technological problem or an economic problem?Is there confusion on the definition of decentralisation? Is it better to focus on openness and non-excludability?Are the incentive mechanisms in many blockchains broken? Should all budget be focused on security, what about scalability and decentralisation?What is fee recycling attack? Which blockchain solves it? This is not just a guide; it's a roadmap for those seeking a deeper understanding of the technological nuances that differentiate blockchains.

This CoPart is a dual complement to Visual Category Theory Brick by Brick, Part 3. It covers adjoint functors, diagram shapes and categories, cones and cocones, limits and colimits, pullbacks and pushouts.

DataViz: How to Choose the Right Chart for Your Data is a short guide to all the different types of charts you’ll commonly encounter in statistics.It is a snappy little non-threatening book about everything you ever wanted to know about the craft of creating inspirational graphics for your study – irrespective of your audience.

This CoPart is a dual complement to Visual Category Theory Brick by Brick, Part 2. It covers duality, products, coproducts, biproducts, initial and terminal objects, pointed categories, matrix representation of morphisms, and monoids.

This CoPart is a dual complement to Visual Category Theory Brick by Brick, Part 1. The original series translated abstract categorical concepts into the language of LEGO® bricks, and the CoPart series implement the opposite way of translating brick constructions to the standard diagram language of category theory.

The seventh part covers ideas related to functional programming: exponentials, disjoint unions, endofunctors and natural transformations, partial and total functions, monads.

Are you interested in learning about graph theory and applied network analysis, leveraging your Python skills? Then this is the book for you! See how network science & graph theory connects with a variety of data analysis problems, and use it to solve your next data science challenge!