Graph data mining : algorithm, security and application / Qi Xuan, Zhongyuan Ruan, Yong Min, editors.
Xuan, Qi, editor.; Ruan, Zhongyuan, editor.; Min, Yong, editor.
2021
QA76.9.D343 G73 2021
Available Online

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Title Graph data mining : algorithm, security and application / Qi Xuan, Zhongyuan Ruan, Yong Min, editors.
ISBN 9789811626098 (electronic bk.)
981162609X (electronic bk.)
9789811626081
9811626081
DOI https://doi.org/10.1007/978-981-16-2609-8
Published Singapore : Springer, [2021]
Copyright ©2021
Language English
Description 1 online resource : illustrations (some color)
Other Standard Identifiers 10.1007/978-981-16-2609-8 doi
Call Number QA76.9.D343 G73 2021
Dewey Decimal Classification 006.3/12
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed August 2, 2021).
Added Author Xuan, Qi, editor.
Ruan, Zhongyuan, editor.
Min, Yong, editor.
Series Big data management.
Available in Other Form Graph data mining.
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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.

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