TY  - GEN
AU  - Hamilton, William L. 
TI  - Graph representation learning
SN  - 9781681739632
U1  - 006.31 
PY  - 2020///
CY  - California
PB  - Morgan and Claypool Publishers
KW  - Machine learning
KW  - Neural networks
KW  - Graph theory--Data processing
KW  - Deep Generative Models
KW  - Graph-Structured data
N1  - includes bibliography
N2  - This book is a foundational guide to graph representation learning, including state-of-the art advances, and introduces the highly successful graph neural network (GNN) formalism. 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.

https://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1576
ER  -