This book offers a complete study in the area of graph learning in cyber, emphasising graph neural networks (GNNs) and their cyber security applications.
This book offers a complete study in the area of graph learning in cyber, emphasising graph neural networks (GNNs) and their cyber security applications.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Mohamed Abdel-Basset is an Associate Professor at the Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt. Nour Moustafa currently is a Senior Lecturer and Leader of Intelligent Security Group at the School of Engineering and Information Technology, University of New South Wales (UNSW), Canberra, Australia. He is also a Strategic Advisor (AI-SME) at DXC Technology, Canberra. Hossam Hawash is a Senior Researcher at the Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt. Zahir Tari is the Research Director of the RMIT Centre of Cyber Security Research and Innovation (CCSRI), Royal Melbourne Institute of Technology, School of Computing Technologies, Melbourne, Australia.
Inhaltsangabe
1. Introduction to Graph Intelligence. 2. Fundamentals of Graph Representations. 3. Graph Embedding: Methods, Taxonomies, and Applications. 4. Toward Graph Neural Networks: Essentials and Pillars. 5. Graph Convolution Networks: A Journey from Start to End. 6. Graph Attention Networks: A Journey from Start to End. 7. Recurrent Graph Neural Networks: A Journey from Start to End. 8. Graph Autoencoders: A Journey from Start to End. 9. Interpretable Graph Intelligence: A Journey from Black to White Box. 10. Toward Privacy Preserved Graph Intelligence: Concepts, Methods, and Applications
1. Introduction to Graph Intelligence. 2. Fundamentals of Graph Representations. 3. Graph Embedding: Methods, Taxonomies, and Applications. 4. Toward Graph Neural Networks: Essentials and Pillars. 5. Graph Convolution Networks: A Journey from Start to End. 6. Graph Attention Networks: A Journey from Start to End. 7. Recurrent Graph Neural Networks: A Journey from Start to End. 8. Graph Autoencoders: A Journey from Start to End. 9. Interpretable Graph Intelligence: A Journey from Black to White Box. 10. Toward Privacy Preserved Graph Intelligence: Concepts, Methods, and Applications
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