Big Data of Complex Networks

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

• Publisher: Chapman and Hall/CRC
• Year: 2016
• Print ISBN: 9781498723619
• eBook ISBN: 9781498723626
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Mining
• Index: Computer Science

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.

Key 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

• 
  
  

Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany. Currently, he is Professor at UMIT – The Health and Life Sciences University, Austria, and the Universität der Bundeswehr München. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory.

Frank Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Technology, Finland. His research interests are in the field of computational biology, machine learning and network medicine.

Stefan Pickl holds a PhD in mathematics from the Darmstadt University of Technology, and is currently a Professor at Bundeswehr Universität München. His research interests are in operations research, systems biology, graph theory and discrete optimization.

Andreas Holzinger received his PhD in cognitive science from Graz University and his habilitation (second PhD) in computer science from Graz University of Technology. He is head of the Holzinger Group HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology.

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Editors
• Contributors
• 1: Network Analyses of Biomedical and Genomic Big Data
• 2: Distributed or Network-Based Big Data?
• 3: Big Data Text Automation on Small Machines
• 4: Big Data Visualization for Large Complex Networks
• 5: Finding Small Dominating Sets in Large-Scale Networks
• 6: Techniques for the Management and Querying of Big Data in Large-Scale Communication Networks
• 7: Large Random Matrices and Big Data Analytics
• 8: Big Data of Complex Networks and Data Protection Law: An Introduction to an Area of Mutual Conflict
• 9: Structure, Function, and Development of Large-Scale Complex Neural Networks
• 10: ScaleGraph: A Billion-Scale Graph Analytics Library
• 11: Challenges of Computational Network Analysis with R
• 12: Visualizing Life in a Graph Stream
• Index