8 Groundbreaking New Graphs Books Defining 2025

Jeremy Kubica, drawing from his deep experience in software development, offers a fresh take on graph algorithms by weaving humor and imaginative examples into the technical content. You’ll learn to implement classic algorithms like Dijkstra’s, A*, and Floyd-Warshall with Python code that’s both accessible and practical. The book goes beyond basics, covering topics such as topological sorting, minimum spanning trees, and the traveling salesperson problem, with scenarios that make abstract concepts tangible. This approach benefits programmers and students who want to build strong intuition for when and how to apply graph algorithms in real-world projects.

After analyzing extensive research and historical developments, the authors present a focused examination of graph decompositions that goes beyond basic theory. You’ll explore a range of decomposition techniques, from cyclic structures like cycles and digraphs to acyclic forms such as trees and stars, supported by over 500 theorems and 56 conjectures. The book also tackles recent innovations in breaking down graphs into multiple subgraph types, making it a detailed resource for those aiming to deepen their understanding of graph structures. This is best suited for advanced students, researchers, and enthusiasts who want a rigorous, theorem-rich reference rather than a beginner’s overview.

This tailored book explores the latest breakthroughs in graph algorithms and theory emerging in 2025, designed to align with your current skills and interests. It reveals cutting-edge developments and novel approaches that are shaping the future of graph science, focusing on topics you care about most. Through a personalized lens, the content matches your background and specific goals, making complex advancements accessible and directly relevant. The book examines new discoveries in algorithmic design, network modeling, and data relationships, providing an engaging journey through the evolving landscape of graphs. By narrowing in on your priorities, it ensures efficient learning and deep understanding of this dynamic field.

Nexus AI is a leading authority in graph databases whose deep expertise shines through in this guide to Neo4j. You’ll explore crucial areas like graph theory fundamentals, Cypher query language, and graph algorithms, gaining clear, practical skills through numerous code examples and tutorials. The book also dives into data modeling and performance tuning, making it useful not just for beginners but for practitioners aiming to optimize complex data relationships. If you want to understand how to implement and leverage graph databases effectively, especially Neo4j, this book offers a focused, hands-on approach without unnecessary fluff.

What happens when expertise in data science meets graph theory? Colleen M Farrelly and her co-authors present a methodical guide to transforming various data types into networks and applying graph algorithms using Python. You’ll learn to tackle problems like fake news spread prediction and epidemic tracking through case studies that move beyond theory into practice. The book also explores advanced topics such as graph databases and graph neural networks, making it a solid resource for those with basic Python skills eager to explore network science’s practical side. If you want a book that bridges foundational knowledge with emerging techniques in graph theory, this fits the bill without overselling its scope.

Unlike most graph theory books that focus broadly on general concepts, this work dives deeply into the specialized area of matching covered graphs, tracing the evolution of the field from W.T. Tutte's foundational insights in 1947 to current advances. You gain a structured understanding of perfect matchings and their critical role in graph connectivity, enriched by numerous examples and clear illustrations that aid comprehension. This book suits you if you're an undergraduate or graduate student seeking both theoretical grounding and new research directions in matching theory, particularly around the challenging unsolved problems it highlights. Its organized approach not only consolidates known results but also invites exploration beyond the established boundaries.

This tailored book explores the evolving landscape of graph theory and applications as they are projected to develop through 2025 and beyond. It covers emerging graph techniques and innovative tools, focusing on your specific interests and background to deliver content that matches your goals. You will discover the latest discoveries in graph algorithms, network models, and data structures, gaining insights that reflect your role and expertise level. By concentrating on trends shaping the future of graphs, this personalized guide invites you to engage deeply with new research and advancements. It reveals novel approaches and methods that are beginning to influence how graphs are used across computing, data science, and complex network analysis.

What started as Timothy Eastridge's deep dive into graph analytics for fraud detection evolved into a guide that blends Python programming with Neo4j's graph database technology to unlock complex data relationships. You learn to import, query, and visualize graph data, harness graph algorithms, and even integrate Large Language Models like ChatGPT for recommendation systems. Chapters like "Neo4j Vector Index and Retrieval-Augmented Generation" reveal cutting-edge ways to enhance data insights. If you work with interconnected datasets and want to elevate your analytics beyond traditional methods, this book offers practical projects and frameworks tailored for your needs.

Tomaž Bratanic is a network scientist at heart, working where graphs meet machine learning, which clearly informs this book’s approach. You’ll learn to construct and analyze graphs using both structured and unstructured data, including how to model knowledge graphs, apply social network algorithms like PageRank, and integrate graph features into machine learning workflows with node embeddings. The book balances theory with practical projects, such as analyzing Twitter data and building graphs via NLP techniques, making complex concepts approachable even if you’re new to graph data science. If you’re aiming to harness graph algorithms for data insights or AI applications, this book offers concrete methods without unnecessary jargon.

After decades of research at Eindhoven University of Technology, Remco van der Hofstad developed this volume to address the local structures within random graph models that mirror real-world networks like the Internet and citation systems. You’ll explore when these models exhibit giant components and the concept of small- and ultra-small-world phenomena, gaining a rigorous understanding of local convergence techniques essential for analyzing sparse random graphs. The book is dense, with exercises that sharpen your intuition and chapters that discuss community structures and directed models, making it ideal for researchers or PhD students ready to deepen their grasp of complex network theory. If you want a mathematically rigorous yet applied perspective on modern network analysis, this book offers a direct path.