Dehmer Matthias - Big data of complex networks

BIG DATA

OF COMPLEX

NETWORKS

Chapman & Hall/CRC

Big Data Series

SERIES EDITOR

Sanjay Ranka

AIMS AND SCOPE

This series aims to present new research and applications in Big Data, along with the computational tools and techniques currently in development. The inclusion of concrete examples and applications is highly encouraged. The scope of the series includes, but is not limited to, titles in the areas of social networks, sensor networks, data-centric computing, astronomy, genomics, medical data analytics, large-scale e-commerce, and other relevant topics that may be proposed by potential contributors.

PUBLISHED TITLES

BIG DATA COMPUTING: A GUIDE FOR BUSINESS AND TECHNOLOGY MANAGERS

Vivek Kale

BIG DATA OF COMPLEX NETWORKS

Matthias Dehmer, Frank Emmert-Streib, Stefan Pickl, and Andreas Holzinger

BIG DATA : ALGORITHMS, ANALYTICS, AND APPLICATIONS

Kuan-Ching Li, Hai Jiang, Laurence T. Yang, and Alfredo Cuzzocrea

NETWORKING FOR BIG DATA

Shui Yu, Xiaodong Lin, Jelena Mii, and Xuemin (Sherman) Shen

Chapman & Hall/CRC
Big Data Series

BIG DATA

OF COMPLEX

NETWORKS

Edited by

Matthias Dehmer

UMIT - The Health & Life Sciences

University, Hall in Tyrol, Austria

and

Nankai University, Tianjin, China

Frank Emmert-Streib

Queens University Belfast, UK

Stefan Pickl

Universitaet der Bundeswehr
Muenchen, Neubiberg, Germany

Andreas Holzinger

Medical University Graz, Austria

CRC Press

Taylor & Francis Group

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2017 by Taylor & Francis Group, LLC

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No claim to original U.S. Government works

Printed on acid-free paper

Version Date: 20160609

International Standard Book Number-13: 978-1-4987-2361-9 (Hardback)

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Library of Congress Cataloging-in-Publication Data

Names: Dehmer, Matthias, 1968- editor. | Emmert-Streib, Frank, editor. | Pickl, Stefan, 1967- editor. | Holzinger, Andreas, editor.

Title: Big data of complex networks / editors, Matthias Dehmer, Frank Emmert-Streib, Stefan Pickl, and Andreas Holzinger.

Description: Boca Raton : CRC Press, 2016. | Series: Chapman & Hall/CRC big data series | Includes bibliographical references and index.

Identifiers: LCCN 2016003196 | ISBN 9781498723619 (hardback : alk. paper)

Subjects: LCSH: Big data. | Large scale systems. | System analysis.

Classification: LCC QA402 .B485 2016 | DDC 005.7--dc23

LC record available at http://lccn.loc.gov/2016003196

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Contents

Mayur Sarangdhar, Ranga Chandra Gudivada, Rasu B. Shrestha, Yunguan Wang, and Anil G. Jegga

Bo Hu and Klaus Arto

Ming Jia, Yiqi Bai, Jingwen Wang, Wenjing Yang, Hao Zhang, and Jie Wang

Lei Shi, Yifan Hu, and Qi Liao

Liang Zhao

Yacine Djemaiel and Noureddine Boudriga

Robert Caiming Qiu

Florent Thouvenin

Joaqun J. Torres

Toyotaro Suzumura

Shiwen Sun, Shuai Ding, Chengyi Xia, and Zengqiang Chen

James Abello, David DeSimone, Steffen Hadlak, Hans-Jrg Schulz, and Mika Sumida

Today, Big Data affects practically every scientist in any domain, from astronomy to zoology. Data science is meanwhile seen as key in the investigation of our nature, from the microcosm to the macrocosm. Big Data actually reverses the classical scientific hypothetic-deductive approach, hence data science itself produces unprecedented amounts of Big Data.

However, in certain domains, for example, in the biomedical domain, we are confronted not only with enormously Big Data, but with complex data. The increasing trend toward personalized medicine has resulted in an explosion in the amount of complex data, for example, from genomics, proteomics, metabolomics, transcriptomics, lipidomics, fluxomics, phenomics, microbiomics, epigenetics, and so on.

Here the science of networks can be of great help because much of this Big Data is available in the form of point clouds in arbitrarily high dimensions, which consequently lets us make use of the great benefits of graph theorya prime object of discrete mathematics with sheer endless application possibilities and many open future research avenues.

The main goal of the book Big Data of Complex Networks is to present and demonstrate existing and novel approaches for handling methods from Big Data for analyzing networks. The underlying mathematical methods have been developed with the aid of graph theory, Big Data, general 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, including

Big Data analysis for biological networks

Big Data analytics for storage and processing of servers by means of complex networks

Big Data text analysis by using networks

Network visualization for Big Data

Big Data querying in large networks