Download the fantastic book titled Graph Representation Learning written by William L. William L. Hamilton, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Graph Representation Learning", which was released on 01 June 2022. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Computers genre.
Summary of Graph Representation Learning by William L. William L. Hamilton PDF
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.
Detail About Graph Representation Learning PDF
- Author : William L. William L. Hamilton
- Publisher : Springer Nature
- Genre : Computers
- Total Pages : 141 pages
- ISBN : 3031015886
- Release Date : 01 June 2022
- PDF File Size : 15,8 Mb
- Language : English
- Rating : 4/5 from 21 reviews
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