Coming soon · Book Profile
Networks_ A Very Short Introduction
A concise introduction to network science showing how the hidden web of connections among elements—rather than the elements themselves—governs the behavior of social, biological, technological, and economic systems.
A profile of this book is on the way.
What it’s about
Networks: A Very Short Introduction reveals that whether you are looking at ecosystems, the Internet, financial markets, the brain, or human friendships, the key to understanding their behavior lies in the pattern of connections linking their parts. Caldarelli and Catanzaro guide readers from Euler's bridges and Moreno's social diagrams to modern discoveries about small worlds, scale-free hubs, preferential attachment, and self-organization, demonstrating that wildly different systems obey strikingly similar mathematical laws. Through vivid examples—cod collapses, six degrees of separation, the Kevin Bacon game, financial dominoes, plagues, and blackouts—the book teaches a network point of view that exposes emergent, complex, and self-organized phenomena invisible to traditional reductionist analysis, while honestly mapping the limits and ethical risks of the approach.
The through-line
- Who it’s for
- A curious reader—student, professional, or scientist—who wants to understand how interconnected systems in nature, technology, and society actually work.
- The problem
- Complex systems like markets, ecosystems, epidemics, and the Internet behave in surprising ways that defy element-by-element analysis. The reader feels overwhelmed and powerless before phenomena that seem chaotic, unpredictable, and beyond comprehension.
- The plan
- Adopt a network point of view: see systems as graphs of nodes and links.
- Learn the foundational ideas—graphs, degree, directed and weighted links.
- Recognize universal structures: giant components, small worlds, hubs, and scale-free distributions.
- Understand the mechanisms—preferential attachment, fitness, homophily—that generate networks.
- Dig deeper into correlations, clustering, centrality, and communities.
- The payoff
- The reader perceives large-scale structures and short paths connecting apparently unrelated elements. · The reader can explain emergent and self-organized phenomena rather than being mystified by them. · The reader applies network insights to public health, technology, business, or research more effectively.
See our guide
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Additional reading
- OpenAI Prompt Engineering Guide · OpenAI
Official documentation from the model provider on how to craft effective prompts, recommended by the author.
- Anthropic's Prompt Engineering Guide · Anthropic
Official guide from another major LLM provider, offering best practices for prompt design.
- dair-ai/Prompt-Engineering-Guide · DAIR.AI Community
A comprehensive, community-maintained GitHub repository covering a wide range of prompt engineering techniques.
- GPT Researcher · assafelovic
An open-source project that inspired the book's research summarization engine, recommended for further exploration of building production-grade LLM applications.
- Building Effective AI Agents · Anthropic
An article recommended by the author for a deeper understanding of the distinction between agentic workflows and autonomous agents.
- Model Context Protocol (MCP) Official Website · Anthropic and Community
The primary source for the MCP specification, tutorials, and ecosystem resources, essential for building or consuming MCP servers.
- FastMCP 2 Documentation · jlowin and community
The official documentation for the recommended Python SDK for building MCP servers, used in the book's example.
- Knowledge Graph Enhanced RAG · Tomaž Bratanič and Oskar Hane
Recommended by the author for readers interested in diving deeper into combining knowledge graphs with RAG.
- Database Management Systems · Gehrke and Ramakrishnan
Referenced in Chapter 2 as a resource for readers unfamiliar with the basics of SQL and Relational Database Management Systems (RDBMSs), which are foundational for the structured data analysis chapters.
- Survey of Hallucination in Natural Language Generation · Ji, Z., Lee, N., Frieske, R., et al.
Cited in Chapter 2 to explain the concept of 'hallucinations' in LLMs and to underscore the importance of verifying information provided by models.