Dehmer, M., Emmert-Streib, F., Pickl, S. & Holzinger, A. (eds.) 2016. Big Data of Complex Networks, Boca Raton, London, New York: CRC Press Taylor & Francis Group. ISBN 9781498723619 [CRC Press], [CRCnetBASE], [Amazon].

Today, Big Data affects every scientist in any domain, from **A**stronomy to **Z**oology. Data science is meanwhile seen as key in the investigation of our nature, from the microcosm to the macrocosm. Big Data reverses the classical scientific hypothetic-deductive approach, hence data science itself produces huge amounts of Big Data. However, in certain domains, for example, in the biomedical domain, we are confronted not only with Big Data, but with complex data, e.g. the increasing trend toward personalized medicine has resulted in an explosion in the amount of complex data. Here the **science of networks** can be of great help because much of this large data sets are available in the form of point clouds in arbitrarily high dimensions, which consequently lets us make use of the great benefits of **graph theory **— a prime object of discrete mathematics with sheer endless application possibilities and many open future research avenues. The main goal of this book is to present approaches for handling methods for analyzing big data networks. The underlying mathematical methods have been developed with the aid of graph theory, computer science, data analysis, machine learning, and statistical techniques.

This book is intended for researchers and graduate and advanced undergraduate students in fields including mathematics, computer science, physics, bioinformatics, and systems biology. Of course, as the potential of Big Data methods has been huge, this list of scientific fields cannot be complete and will hopefully be extended in the future. The topics addressed in this book cover a broad range of Big Data concepts and methods applied to complex networks.

18-08-2016

### Features

- Provides a complete discussion of both the hardware and software used to organize big data
- Describes a wide range of useful applications for managing big data and resultant data sets
- Maintains a firm focus on massive data and large networks
- Unveils innovative techniques to help readers handle big data

Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks.

Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, **Big Data of Complex Networks **is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics.

- Provides a complete discussion of both the hardware and software used to organize big data
- Describes a wide range of useful applications for managing big data and resultant data sets
- Maintains a firm focus on massive data and large networks
- Unveils innovative techniques to help readers handle big data