This textbook provides an exciting new addition to the area of network science featuring a
stronger and more methodical link of models to their mathematical origin and explains how these
relate to each other with special focus on epidemic spread on networks. The content of the book
is at the interface of graph theory stochastic processes and dynamical systems. The authors
set out to make a significant contribution to closing the gap between model development and the
supporting mathematics. This is done by: Summarising and presenting the state-of-the-art in
modeling epidemics on networks with results and readily usable models signposted throughout the
book Presenting different mathematical approaches to formulate exact and solvable models
Identifying the concrete links between approximate models and their rigorous mathematical
representation Presenting a model hierarchy and clearly highlighting the links between model
assumptions and model complexity Providing a reference source for advanced undergraduate
students as well as doctoral students postdoctoral researchers and academic experts who are
engaged in modeling stochastic processes on networks Providing software that can solve
differential equation models or directly simulate epidemics on networks. Replete with numerous
diagrams examples instructive exercises and online access to simulation algorithms and
readily usable code this book will appeal to a wide spectrum of readers from different
backgrounds and academic levels. Appropriate for students with or without a strong background
in mathematics this textbook can form the basis of an advanced undergraduate or graduate
course in both mathematics and other departments alike.
