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This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data.…
Presents both theory and applications of network models and addresses computational issues
Describes a wide range of modern methods for analysing complex networks, including measures for comparing huge networks and methods for analysing social and biological networks that change over time
Real-world applications from technological networks, information networks, financial networks, and social networks show the concepts and methods in action
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Author
Alan Julian Izenman,Temple University, Philadelphia
Alan J. Izenman is Professor of Statistical Science at Temple University. He received his Ph.D. from the University of California, Berkeley. He was a faculty member at Tel Aviv University and Colorado State University, and was a visiting faculty member at the University of Chicago, the University of Minnesota, and Stanford University. He was Program Director of Statistics and Probability at NSF (1992-94). A Fellow of the ASA, RSS, and ISI, he has served on the Editorial Boards of JASA, Law, Probability, and Risk, and Statistical Analysis and Data Mining. He is the author of Modern Multivariate Statistical Techniques (2013).