Graph Data Mining Algorithm, Security and Application /
Graph Data Mining Algorithm, Security and Application / [electronic resource] :
edited by Qi Xuan, Zhongyuan Ruan, Yong Min.
- 1st ed. 2021.
- XVI, 243 p. 92 illus., 67 illus. in color. online resource.
- Big Data Management, 2522-0187 .
- Big Data Management, .
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.
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. .
9789811626098
10.1007/978-981-16-2609-8 doi
Data mining.
Machine learning.
Artificial intelligence--Data processing.
Data protection--Law and legislation.
Data Mining and Knowledge Discovery.
Machine Learning.
Data Science.
Privacy.
QA76.9.D343
006.312
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.
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. .
9789811626098
10.1007/978-981-16-2609-8 doi
Data mining.
Machine learning.
Artificial intelligence--Data processing.
Data protection--Law and legislation.
Data Mining and Knowledge Discovery.
Machine Learning.
Data Science.
Privacy.
QA76.9.D343
006.312