The increased and widespread availability of large network data resources in recent years has resulted in the increased need for effective methods for the analysis of these networks. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straight forward task because of the size of the data sets and the computer power required for the analyses. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This cutting-edge text introduces graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this inter-disciplinary text provides an ideal introduction to and insight into the field of network data analysis.