Graph Data Mining Algorithm, Security and Application /
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 discove...
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| Other Authors / Creators: | Xuan, Qi. editor. Ruan, Zhongyuan. editor. Min, Yong. editor. |
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| Other Corporate Authors / Creators: | SpringerLink (Online service) |
| Format: | Electronic eBook |
| Language: | English |
| Edition: | 1st ed. 2021. |
| Imprint: | Singapore : Springer Nature Singapore : Imprint: Springer, 2021. |
| Series: | Big Data Management,
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| Subjects: | |
| Online Access: | Available in Springer Computer Science eBooks 2021 English/International. |
Table 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.