An emerging trend in many scientific disciplines is a strong tendency toward being
transformed into some form of information science. One important pathway in this
transition has been via the application of network analysis. The basic methodology in
this area is the representation of the structure of an object of investigation by a graph
representing a relational structure. It is because of this general nature that graphs have
been used in many diverse branches of science including bioinformatics, molecular
and systems biology, theoretical physics, computer science, chemistry, engineering,
drug discovery, and linguistics, to name just a few. An important feature of the book
“Statistical and Machine Learning Approaches for Network Analysis” is to combine
theoretical disciplines such as graph theory, machine learning, and statistical data
analysis and, hence, to arrive at a new field to explore complex networks by using
machine learning techniques in an interdisciplinary manner.