Big Data of Complex Networks : Chapman & Hall/CRC Big Data Series - Matthias Dehmer

eTEXT

Big Data of Complex Networks

By: Matthias Dehmer (Editor), Frank Emmert-Streib (Editor), Stefan Pickl (Editor), Andreas Holzinger (Editor)

eText | 19 August 2016 | Edition Number 1

At a Glance

eText


$104.49

or 4 interest-free payments of $26.12 with

 or 

Instant online reading in your Booktopia eTextbook Library *

Read online on
Desktop
Tablet
Mobile

Not downloadable to your eReader or an app

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

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 Universitat der Bundeswehr Munchen. 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 Universitat Munchen. 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.

Read online on
Desktop
Tablet
Mobile

More in Data Mining

Data Engineering for Cybersecurity - James Bonifield

eBOOK

RRP $69.92

$55.99

20%
OFF
Graph Learning Techniques - Baoling Shan

eTEXT