Overview
- Provides a comprehensive overview of the state-of-the-art in graph data mining algorithms
- Introduces various key applications of the advanced graph data mining techniques
- Presents robust graph data mining based on subgraph networks and graph augmentation
Part of the book series: Big Data Management (BIGDM)
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Table of contents (11 chapters)
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Front Matter
Keywords
About this book
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.
Editors and Affiliations
About the editors
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 ofTechnology, 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.Bibliographic Information
Book Title: Graph Data Mining
Book Subtitle: Algorithm, Security and Application
Editors: Qi Xuan, Zhongyuan Ruan, Yong Min
Series Title: Big Data Management
DOI: https://doi.org/10.1007/978-981-16-2609-8
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
Hardcover ISBN: 978-981-16-2608-1Published: 16 July 2021
Softcover ISBN: 978-981-16-2611-1Published: 17 July 2022
eBook ISBN: 978-981-16-2609-8Published: 15 July 2021
Series ISSN: 2522-0179
Series E-ISSN: 2522-0187
Edition Number: 1
Number of Pages: XVI, 243
Number of Illustrations: 25 b/w illustrations, 67 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Machine Learning, Data Structures and Information Theory, Artificial Intelligence, Privacy