Networks are part of our lives. For example, our relationships with family and friends assemble networks; food chains are a type of network. In a network structure, some components are tightly connected, forming a community. The topic of this book is the analysis of community structures.
Chapter 1 introduces the topic to readers, with an emphasis on various types of community structures, including static and dynamic structures. Community structures can be quantitatively evaluated, and the author introduces the measurements associated with quantitative analysis.
Chapter 2 presents algorithms to identify community structures. Some communities are large, and some are small. Some communities overlap. This chapter introduces an algorithm to identify such structures, as well as hierarchical community structures. Chapter 3 considers the analysis of networks in different scales. Multiscale analysis has been widely applied in other fields, such as image processing. The idea is to analyze the data from a broad perspective and then in increasing levels of detail. Similarly, the concept can be applied to network analysis. The author not only presents algorithms, but also provides a case study where the algorithms can be applied for multiscale analysis.
Chapter 4 addresses dynamic networks and community structures. Networks can change and tend to be dynamic most of the time. When we gain new friends, our friend network expands. Some concepts of physical sciences, such as spectrum, diffusion, and conductance, can be extended to analyzing network dynamics. The author introduces these concepts and their analysis models. The last chapter, chapter 5, discusses structural regularities and brings in probabilities for community structure analysis. A community may consist of multiple smaller groups; this chapter presents an analysis framework for such structures using probabilistic models.
In summary, this book provides a unique viewpoint on network analysis. It is a good handbook for engineers specializing in modern network analysis.