Network Science: Analysis and Optimization Algorithms for Real-World Applications

A Practical Guide to Networks, Graphs, and Optimization

Network Science: Analysis and Optimization Algorithms for Real-World Applications brings together two sides of modern network work: understanding what a graph reveals and deciding what to do with it. Written by Carlos Andre Reis Pinheiro and published by Wiley in 2023, the book moves from foundational concepts to concrete algorithms for analysis and optimization, with examples drawn from realistic applications.

If you work with connected data—social networks, supply chains, communication systems, or operational models—the value lies in learning how to ask better questions of the structure and then apply the right procedure to answer them. This guide is built for that kind of practical fluency.

Concepts, Subnetworks, and Network Centralities 📊

Early chapters establish the vocabulary and mechanics of network science: history, characteristics, small-world properties, random graphs, and the data structures used for network analysis. From there, the book explores subnetwork analysis, including connected and biconnected components, communities, k-cores, reach networks, projection, node similarity, and subgraph pattern matching.

A substantial section is devoted to centrality measures. Degree, influence, clustering coefficient, closeness, betweenness, eigenvector, PageRank, and hub-and-authority scores are presented as tools for identifying power, influence, and structural importance. The treatment also considers centrality calculations by group, which matters when one network contains distinct populations or segments.

Optimization Algorithms for Real-World Decisions ⚙️

Later chapters shift from description to decision-making. Topics include cliques, cycles, linear assignment, minimum-cost network flow, maximum flow, minimum cut, minimum spanning tree, path finding, shortest paths, and transitive closure. These are the procedures behind routing, matching, resource allocation, distribution, and other operational problems.

Because the author is affiliated with SAS Institute and the book includes SAS Viya context, readers who use SAS can connect the methods to a production analytics environment. The emphasis remains on algorithms and applications rather than on abstract proofs alone.

Who This Book Is For

Network Science is suited to data scientists, analysts, operations researchers, graduate students, and professionals who need to analyze connected data and implement optimization routines. It can serve as a reference for practitioners who already understand basic graph concepts and want a structured path through both analysis and optimization. Instructors may also find it useful for applied courses, though it is not a light introduction; the material is technical and assumes quantitative comfort.

Why Keep It on Your Shelf

Many resources cover either network analysis or optimization. This book treats them as connected stages in the same workflow. The result is a focused Wiley reference for readers who want to move from network structure to actionable solutions, with algorithms presented in a way that supports real-world application. Digital Delights offers this edition for readers who prefer an accessible digital copy for study and reference.