Graph Data Mining : Algorithm, Security and Application
Overview
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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Details
- ISBN-13: 9789811626081
- ISBN-10: 9811626081
- Publisher: Springer
- Publish Date: July 2021
- Dimensions: 9.21 x 6.14 x 0.63 inches
- Shipping Weight: 1.19 pounds
- Page Count: 243
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