Heterogeneous Graph Representation Learning and Applications (Artificial Intelligence: Foundations, Theory, and Algorithms)
by Chuan Shi, Xiao Wang, Philip S. Yu
This book explores heterogeneous graph representation learning through the lenses of artificial intelligence foundations, theory, and algorithms. It provides a focused technical resource for understanding graph-based representation methods and their applications, making it relevant to readers studying modern machine learning and graph analytics.
About This Book
Heterogeneous Graph Representation Learning and Applications presents a focused treatment of heterogeneous graphs within artificial intelligence.
The book examines representation learning for graph structures containing varied types of information, with attention to foundations, theory, and algorithms.
It offers a technical resource for readers studying how graph-based methods support artificial intelligence research and applications.
Reviews
No reviews yet. Be the first to review this book!