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Algorithms and models for network data and link analysis
Fouss F., Saerens M., Shimbo M., Cambridge University Press, New York, NY, 2016. 543 pp. Type: Book (978-1-107125-77-3)
Date Reviewed: Sep 8 2017

A recent article in The Economist noted that data is to the twenty-first century what oil was to the twentieth century. Data is almost ubiquitously generated by humans as part of their everyday interactions on social networks, power grids, and document citations. Across various domains of interest, the challenge is to analyze vast amounts of data produced and draw meaningful inferences about the underlying network behavior. The data flow across various types of networks can be effectively captured using the so-called (network) graph, which contains a set of nodes (that correspond to origin or termination points for data) and a set of links (that correspond to the path traversed by data from its source to the destination). Graphs that result from given data can be quite complex and therefore warrant usage of computationally efficient algorithms for their analysis, which indeed is the primary focus of this work. This monograph consciously restricts itself to algorithms aimed at static networks, whose characteristics do not change with time. The analytic tools or techniques come from the broad realm of computer science and engineering.

After an introductory chapter on basic graph theory, the book is logically divided into two parts. The first part, comprising chapters 2 to 5, provides analysis for characterizing basic network elements like nodes and edges. For example, chapters 2 and 3 develop similarity/dissimilarity measures between various network nodes, which can then further be used for analysis like link prediction and clustering. On the other hand, chapters 4 and 5 develop measures aimed at characterizing the “primacy,” or lack of it, for a given network node. The second part, comprising chapters 6 to 10, provides tools for analyzing global network structure. These chapters are devoted to discussion of techniques aimed at tasks like node labeling and node clustering. The selection of algorithms for discussion in these chapters is based on the following: well-established nature, optimality guarantees, and scalability.

In conclusion, this monograph delves into the various models and algorithms required for the analysis of network data. The contents are centered on tasks that one aims to accomplish as part of his or her data analysis exercise. The models and algorithms presented in this book will be of great use to both graduate students and seasoned researchers working in areas like data mining and pattern recognition. Indeed, the book would be useful for any assiduous researcher of network science.

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Reviewer:  Laxminarayana Pillutla Review #: CR145528 (1711-0692)
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Network Management (C.2.3 ... )
 
 
Data Mining (H.2.8 ... )
 
 
Graph Theory (G.2.2 )
 
 
Pattern Recognition (I.5 )
 
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