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Big data of complex networks
Dehmer M., Emmert-Streib F., Pickl S., Holzinger A., Chapman & Hall/CRC, Boca Raton, FL, 2016. 332 pp. Type: Book (978-1-498723-61-9)
Date Reviewed: Nov 8 2019

As mentioned in the preface, big data affects every scientist and every domain. This edited book aims at filling gaps in the area of big data of complex networks, from a computer scientist, statistician, and mathematician perspective. The book is research oriented and comprised of 12 chapters, ranging from conceptual and applied to quite mathematical and abstract styles.

In chapter 1, the contributors recognize the large heterogeneous deluge of biomedical and genomic data and advocate a collaborative and interdisciplinary approach. Many repositories of information are mentioned and a solid set of references is included. Most chapters in the book, even the short ones, have a research inclination and the references are overall quite comprehensive.

In chapter 2, the contributors address distributed and network-based big data. They promote a loosely connected system of nodes supportive of decentralized storage and processing. Chapter 3 illustrates big data text automation on small machines. The authors use two cases: online social networks and news media websites. Both cases use Chinese organizations; the processing is done in a small lab, but processes large amounts of data. The chapter mentions the application of their algorithms and techniques to help a medical software company collect, classify, and process medical information.

The fourth chapter discusses visualization techniques to display big data. The authors cover examples of theory, algorithms, frameworks, and systems, and recognize that many additional questions still remain unanswered. Chapter 5 focuses on finding small dominating sets in large-scale networks, and demonstrates sieve and quasi-greedy algorithms.

In chapter 6, the contributors model complex networks using graphs, suggest how to optimize big data management, and introduce the processing and monitoring of data in the cloud. Several case studies are used to illustrate service and industrial applications.

Chapter 7 is the longest and most abstract mathematical chapter in the book. It assumes a reader with a very advanced statistical and mathematics background and would be incomprehensible for an undergraduate student. It sometimes reads more like a mathematics monograph than a chapter.

Chapter 8 is a short conceptual chapter that shows the dichotomy of big data and the tension between big data applications and privacy principles. Chapter 9 presents the structure, function, and development of complex neural networks.

Chapter 10 describes the ScaleGraph software and compares it to alternatives such as the Parallel Boost Graph Library (PBGL), Google Pregel, Apache Giraph, and GraphLab. Starting with the X10 programming language, the author then moves on to explain how ScaleGraph adds a layer of functionality. The three main components are outlined: the XPregel framework, basic linear algebra subprograms (BLAS), and file input/output (I/O).

Chapter 11 covers the challenges of computational network analysis with R. In this context, R is not meant to represent the programming language, but stands for the robustness of a system.

The book ends with another chapter on visualization. It shows a view of graph streams as co-occurrence graphs and isolated group patterns such as herding and straying.

Overall, the book parses a number of heterogeneous topics within the boundaries of “big data of complex networks.” It tries to fill some gaps in the literature. In some ways, the editors have attempted a somewhat too ambitious goal and readers are more likely to read the chapters in a piecemeal fashion.

The book targets a sophisticated audience of computer scientists, statisticians, and applied mathematicians. I would recommend the book as a timely stepping stone in this area. Researchers at the intersection of complex networks and big data will also benefit from the book, and will be able to enrich their research through the thorough references in each chapter.

Reviewer:  Jean-Pierre Kuilboer Review #: CR146765 (2002-0019)
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Database Applications (H.2.8 )
 
 
Content Analysis And Indexing (H.3.1 )
 
 
Systems (H.2.4 )
 
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