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.
This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs--a nascent but quickly growing subset of graph representation learning.
0 有用 瑶光 2021-06-17 20:40:31
非常好,第一次觉得英文书比相关的中文资料好懂
1 有用 虫放 2022-08-18 16:25:47
综述集
0 有用 天瑜 2022-11-30 17:56:57 中国香港
"The field has transformed from a small subset of researchers working on a relatively niche topic to one of the fastest-growing sub-areas of deep learning." 被前言的叙事感动到了。
0 有用 Unknown 2022-06-14 18:08:43
有趣,但对我来说没有用,我还是naive了
0 有用 T128 2024-09-24 17:20:57 瑞典
短小精悍的书,入门gnn首选