''This book presents methods for analyzing graphs and networks quantitatively. Incorporating interdisciplinary knowledge from graph theory, information theory, measurement theory, and statistical techniques, it covers a wide range of quantitative graph-theoretical concepts and methods, including those pertaining to random graphs. Through its broad coverage, the book fills a gap in the contemporary literature of
discrete and applied mathematics, computer science, systems biology, and related disciplines''--''Graph-based approaches have been employed extensively in several disciplines such as biology, computer science, chemistry, and so forth. In the 1990s, exploration of the topology of complex networks became quite popular and was triggered by the breakthrough of the Internet and the examinations of random networks. As a consequence, the structure of random networks has been explored using graph-theoretic methods and stochastic growth models. However, it turned out that besides exploring random graphs, quantitative approaches to analyze networks are crucial as well. This relates to quantifying structural information of complex networks by using ameasurement approach. As demonstrated in the scientific literature, graph- and informationtheoretic measures, and statistical techniques applied to networks have been used to do this quantification. It has been found that many real-world networks are composed of network patterns representing nonrandom topologies.Graph- and information-theoretic measures have been proven efficient in quantifying the structural information of such patterns. The study of relevant literature reveals that quantitative graph theory has not yet been considered a branch of graph theory''--
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