Algorithms and Models for Network Data and Link Analysis

'This is a remarkable book that contains a coherent and unified presentation of many recent network data analysis concepts and algorithms. Rich with details and references, this is a book from which faculty and students alike will learn a lot!' Vincent Blondel, Université Catholique de Louvain, Belgium

'An impressive compilation of motivation, derivations, and algorithms for a wealth of methods relevant to assessing distance and (dis)similarity, importance, labeling, and clustering of network nodes and links - tasks fundamental to network analysis in practice. The gathering of diverse elements from random walks, kernels, and other interrelated topics is particularly welcome.' Eric D. Kolaczyk, Boston University

'This is a reader-friendly up-to-date book covering all the major topics in static network data analysis. It both exposes the reader to the most advanced ideas in the field and provides the researcher with a toolbox of techniques to explore various structures: models involving the graph Laplacian, regularization methods, and Markov interpretations feature in this toolbox, among others.' Pavel Chebotarev, Institute of Control Sciences, Russian Academy of Sciences