ABSTRACT
Networks appear throughout the sciences, forming a common thread linking research activities in many fields, such as sociology, biology, chemistry, engineering, marketing, and mathematics. For example, they are used in ecology to represent food webs and in engineering and computer science to design high-quality Internet router connections. Depending on the application, one network structural property may be more important than another. The structural properties of networks (e.g., degree distribution, clustering coefficient, assortativity) are usually characterized in terms of invariants [8], which are functions on networks that do not depend on the labeling of the nodes. In this chapter, we focus on network invariants that are quantitative, that is, they can be characterized as network measures. Examples
of network measures includes degree-based measures (Randic´ index, assortativity), distance-based measures (Wiener, efficiency complexity), eigenvalue-based measures (Laplacian), and entropy measures [13,14].
