The book provides unified, practical modeling language links graphs, dynamics, statistics, and machine learning to analyze and explain how networks work across disciplines and scales. This textbook explores the intersection of network science and data science. Tailored for an interdisciplinary audience, it provides a gentle introduction to the fundamentals of mathematical modeling and network analysis, integrating graph and systems theory within a unified notational framework. Networks are considered across a wide range of scientific domains and presented as both real-world phenomena and powerful mathematical tools.

From molecules in a cell to social interactions on media platforms, the book illustrates the broad applicability of network modeling through examples drawn from diverse scientific settings. In doing so, it instills an appreciation for robust theoretical frameworks. No specialized background in mathematics, physics, or engineering is assumed.  Aimed at a broad audience, including students in both bachelor’s and master’s programs, the text is well suited for readers in the life sciences, social sciences, and the natural, physical, and engineering sciences. Whether you are a biologist, engineer, or computer scientist, interested in interconnected systems, this book offers a gateway to understanding the language of networks in an increasingly connected world.

Olaf Wolkenhauer received his first degree in systems and control engineering and his PhD for research in possibility theory with applications to data analysis in 1997. He spent over ten years of his academic career in the UK at the University of Manchester Institute of Science and Technology. In 2003, he was appointed a professor at the University of Rostock. Since 2005, he has held an adjunct professorship at Case Western Reserve University in the United States. In 2005, he became a fellow at the Stellenbosch Institute for Advanced Study \(STIAS\).

Saptarshi Bej acquired his master’s degree at IISER-Kolkata in Mathematics, with a specialization in graph theory. He worked as a research assistant briefly at the University of Paderborn, Germany. He joined the Institute of Computer Science at the University of Rostock, Germany, in January 2018. He obtained his doctoral degree from the University of Rostock, Germany, in January 2022. He is currently focusing on developing machine learning algorithms for node representation learning, missing data imputation, anomaly detection, short multivariate time series classification etc. while occasionally exploring their applications in biomedical problems.