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Graph Representation Learning

Graph Representation Learning

Paperback

Series: Synthesis Lectures on Artificial Intelligence and Machine Le

General ComputersGeneral MathematicsProbability & Statistics

ISBN10: 3031004604
ISBN13: 9783031004605
Publisher: Springer
Published: Sep 16 2020
Pages: 141
Weight: 0.63
Height: 0.34 Width: 7.50 Depth: 9.25
Language: English

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis.

Also from

Hamilton, William L.

Also in

General Computers