This book examines graph signal processing as an emerging framework for analysing and interpreting data structured over networks, where relationships between elements are as significant as the data itself. It presents signal processing beyond traditional linear domains, considering how information is represented and manipulated across interconnected systems such as communication networks, sensor arrays, and complex data structures. The narrative reflects on how graph-based models enable a more flexible understanding of signals, allowing patterns, dependencies, and interactions to be captured with greater accuracy. It also explores how cooperative and distributed approaches support efficient data processing, particularly in environments where information is shared across multiple nodes. Attention is given to algorithmic techniques and computational tools that facilitate analysis, filtering, and transformation of signals within graph-based frameworks.

The book also highlights the practical relevance of graph signal processing in modern technological applications, including communication systems, sensing networks, and data analysis. It offers a perspective on how network-based thinking enhances signal processing capabilities, making it valuable for students, researchers, and professionals interested in advanced data and communication systems.