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.
Tabella dei contenuti
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.
Circa l’autore
Qi Xuan is a Professor at the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China. His current research interests include network science, graph data mining, cyberspace security, and deep learning. He has published more than 50 papers in leading journals and conferences, including IEEE TKDE, IEEE TIE, IEEE TNSE, ICSE, and FSE. He is the reviewer of the journals such like IEEE TKDE, IEEE TIE, IEEE TII, and IEEE TNSE.
Zhongyuan Ruan is a lecturer at the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China. His current research interests include network science, such as epidemic and information spreading in complex networks, and traffic networks. He has published more than 20 papers in journals such as Physical Review Letters, Physical Review E, Chaos, Scientific Reports, and Physica A.
Yong Min is an Associate Professor at the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China. His research interests include social network analysis, computational communication, and artificial intelligence algorithms. He was named an Excellent Young Teacher of Zhejiang University of Technology. He has hosted and participated in more than ten projects, including those by national and provincial natural science foundations. He has also published over 30 papers, including two in the leading journal Nature and Science, and he holds more than three patents.
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