Graph Algorithms for Data Science

Discover the power of networks with Tomaž Bratanic’s authoritative guide to graph algorithms tailored for data science. Whether you analyze social media, transportation systems, biological networks or recommendation engines, this book cuts through complexity with clear explanations and practical insight.

What you’ll learn: Core algorithms—shortest paths, spanning trees, centrality measures, community detection, and spectral methods—are presented with intuitive explanations and step-by-step pseudocode. Bratanic balances theory and application, showing how graph algorithms drive real-world analytics, from detecting influential users to optimizing logistics and uncovering hidden structure in large datasets.

Why it matters: Graph data is everywhere. This title helps data scientists, analysts, and researchers translate network theory into actionable models and scalable solutions. Emphasis on computational efficiency and real-data examples prepares you to handle sparse matrices, streaming edges, and large-scale networks common in modern projects.

Who should read it: Practitioners building machine learning pipelines, students expanding their algorithms toolkit, and technical leaders designing data-driven Books will find this book especially valuable. The writing is precise yet approachable, making sophisticated concepts accessible without sacrificing rigor.

Take the next step in your data science journey. Add Tomaž Bratanic’s Graph Algorithms for Data Science to your library and start transforming networked data into strategic insights. Ideal for professionals and researchers worldwide looking for a practical, theory-grounded reference on graph algorithms. Order now to deepen your understanding and accelerate your projects.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.