Big Data of Complex Networks

Name: Big Data of Complex Networks
SKU: GC-1009110799
Availability: InStock

$18.99

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.Big Data of Complex Networks By: Matthias Dehmer Publisher: Chapman & Hall Print ISBN: 9781498723619, 1498723616 other ISBNs: 9781498723626, 1498723624 Copyright year: 2016

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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.Big Data of Complex Networks By: Matthias Dehmer Publisher: Chapman & Hall Print ISBN: 9781498723619, 1498723616 other ISBNs: 9781498723626, 1498723624 Copyright year: 2016

Big Data of Complex Networks

$22.00

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 Ph D 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 Ph D 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 Ph D 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 Ph D in cognitive science from Graz University and his habilitation (second Ph D) 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.Additional ISBNs9780367658434, 9781498723619, 9781315370736, 0367658437, 1498723616, 1315370735, 9781498723619Big Data of Complex Networks 1st Edition is written by Matthias Dehmer and published by Chapman & Hall. ISBNs for Big Data of Complex Networks are 9781498723626, 1498723624 and the print ISBNs are 9780367658434, 0367658437. Additional ISBNs include 0367658437, 1498723616, 1315370735, 9780367658434, 9781498723619, 9781315370736, 9781498723619.

Order Processing Time

Shipping Methods and Carriers

Zetlly partners exclusively with reputable shipping carriers to ensure timely delivery of your orders. We utilize:

FedEx
UPS
USPS

The choice of carrier is determined by factors such as destination, weight, and delivery timeframe to provide optimal service.

Shipping Rates and Fees

Free shipping is provided for all orders over $199.
Orders under $199 will incur a flat-rate shipping fee of $7.99.
All orders shipped within the United States will be subject to a sales tax charge of 5%.

Estimated Delivery Time

Shipping Restrictions

Tracking Your Order

Please allow up to 48 hours for tracking information to update in the carrier’s system.

Eligibility for Returns and Exchanges

How to Return or Exchange an Item

To initiate a return or exchange, please follow these steps:

Contact our customer support at contact@zetlly.com with your order number and reason for return or exchange.
Our team will respond within 24 hours to provide detailed instructions, including the specific Return Address for your shipment.
Package your item securely and include all original packaging and proof of purchase.

Return shipments should be sent to: Blanq LLC 1201 South Hope Street Apt 2413, Los Angeles, CA 90015, USA

Return Conditions

Items must be returned in their original condition, unworn, undamaged, and complete with all original packaging and documentation.
Items returned without prior authorization or not meeting the above conditions may not qualify for a refund or exchange.

Return Shipping Costs

Non-Returnable Items

The following items cannot be returned:

Digital products (e-books or downloadable content)
Personalized or customized items
Gift cards

Accepted Payment Methods

Zetlly accepts the following secure and widely trusted payment options:

PayPal: Easily pay through your PayPal account, benefiting from secure transactions and buyer protection.
Stripe: Pay securely using major credit and debit cards including Visa, MasterCard, American Express, and Discover via Stripe’s encrypted payment gateway.

Payment Security

Zetlly does not store any credit card or sensitive financial information directly on our servers, further enhancing the security and protection of your personal data.

Payment Process and Confirmation

Please retain this confirmation email for your records and reference in case of any inquiries or disputes.

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 Ph D 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 Ph D 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 Ph D 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 Ph D in cognitive science from Graz University and his habilitation (second Ph D) 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.Additional ISBNs9780367658434, 9781498723619, 9781315370736, 0367658437, 1498723616, 1315370735, 9781498723619Big Data of Complex Networks 1st Edition is written by Matthias Dehmer and published by Chapman & Hall. ISBNs for Big Data of Complex Networks are 9781498723626, 1498723624 and the print ISBNs are 9780367658434, 0367658437. Additional ISBNs include 0367658437, 1498723616, 1315370735, 9780367658434, 9781498723619, 9781315370736, 9781498723619.