Exploring Deep Learning Architectures for Graph Applications
(Sprache: Englisch)
Graph-structured data are the backbone of numerous real-world machine learning tasks, such as social networks, recommender systems, traffic networks, and so on. The fundamental challenge in solving these tasks is to find a way to encode graph structures as...
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Graph-structured data are the backbone of numerous real-world machine learning tasks, such as social networks, recommender systems, traffic networks, and so on. The fundamental challenge in solving these tasks is to find a way to encode graph structures as well as to incorporate various node or edge information so that machine learning models can easily exploit them. In this dissertation, we explore deep learning architectures, especially the graph neural networks for multiple graph learning applications, i.e., node classification, link prediction, spatiotemporal graph forecasting on irregular grid, and supervised sequence learning problems.
Autoren-Porträt von Jiani Zhang, Irwin King
Zhang, JianiThe authors are from the department of computer science & engineering at The Chinese University of Hong Kong.
Bibliographische Angaben
- Autoren: Jiani Zhang , Irwin King
- 136 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 6202917652
- ISBN-13: 9786202917650
Sprache:
Englisch
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