- Publisher's listprice EUR 181.89
-
75 438 Ft (71 846 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 20% (cc. 15 088 Ft off)
- Discounted price 60 351 Ft (57 477 Ft + 5% VAT)
Subcribe now and take benefit of a favourable price.
Subscribe
75 438 Ft
Availability
printed on demand
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 1st ed. 2021
- Publisher Springer Nature Singapore
- Date of Publication 17 July 2022
- Number of Volumes 1 pieces, Book
- ISBN 9789811626111
- Binding Paperback
- See also 9789811626081
- No. of pages243 pages
- Size 235x155 mm
- Weight 403 g
- Language English
- Illustrations XVI, 243 p. 92 illus., 67 illus. in color. Illustrations, black & white 279
Categories
Long description:
Graph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used to discover useful information and knowledge from graph data. The complications of nodes, links and the semi-structure form present challenges in terms of the computation tasks, e.g., node classification, link prediction, and graph classification. In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining.
This book provides a state-of-the-art review of graph data mining methods. It addresses a current hot topic – the security of graph data mining – and proposes a series of detection methods to identify adversarial samples in graph data. In addition, it introduces readers to graph augmentation and subgraph networks to further enhance the models, i.e., improve their accuracy and robustness. Lastly, the book describes the applications of these advanced techniques in various scenarios, such as traffic networks, social and technical networks, and blockchains.
MoreTable of Contents:
Chapter 1. Information Source Estimation with Multi-Channel Graph Neural Network.- Chapter 2. Link Prediction based on Hyper-Substructure Network.- Chapter 3. Broad Learning Based on Subgraph Networks for Graph Classification.- Chapter 4. Subgraph Augmentation with Application to Graph Mining.- 5. Adversarial Attacks on Graphs: How to Hide Your Structural Information.- Chapter 6. Adversarial Defenses on Graphs: Towards Increasing the Robustness of Algorithms.- Chapter 7. Understanding Ethereum Transactions via Network Approach.- Chapter 8. Find Your Meal Pal: A Case Study on Yelp Network.- Chapter 9. Graph convolutional recurrent neural networks: a deep learning framework for traffic prediction.- Chapter 10. Time Series Classification based on Complex Network.- Chapter 11. Exploring the Controlled Experiment by Social Bots.
More
