Graph Representation Learning is popular PDF and ePub book, written by William L. William L. Hamilton in 2022-06-01, it is a fantastic choice for those who relish reading online the Computers genre. Let's immerse ourselves in this engaging Computers book by exploring the summary and details provided below. Remember, Graph Representation Learning can be Read Online from any device for your convenience.

Graph Representation Learning Book PDF Summary

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 Book of Graph Representation Learning PDF

Graph Representation Learning
  • Author : William L. William L. Hamilton
  • Release : 01 June 2022
  • Publisher : Springer Nature
  • ISBN : 9783031015885
  • Genre : Computers
  • Total Page : 141 pages
  • Language : English
  • PDF File Size : 9,6 Mb

If you're still pondering over how to secure a PDF or EPUB version of the book Graph Representation Learning by William L. William L. Hamilton, don't worry! All you have to do is click the 'Get Book' buttons below to kick off your Download or Read Online journey. Just a friendly reminder: we don't upload or host the files ourselves.

Get Book

Sparse and Redundant Representations

Sparse and Redundant Representations Author : Michael Elad
Publisher : Springer Science & Business Media
File Size : 22,5 Mb
Get Book
A long long time ago, echoing philosophical and aesthetic principles that existed since antiquity, W...

Mental Representations

Mental Representations Author : Allan Paivio
Publisher : Oxford University Press
File Size : 10,7 Mb
Get Book
In this volume Professor Paivio updates his influential theory of cognition and provides a systemati...

Symmetry Representations and Invariants

Symmetry  Representations  and Invariants Author : Roe Goodman,Nolan R. Wallach
Publisher : Springer Science & Business Media
File Size : 11,5 Mb
Get Book
Symmetry is a key ingredient in many mathematical, physical, and biological theories. Using represen...

Linear Representations of Finite Groups

Linear Representations of Finite Groups Author : Jean-Pierre Serre
Publisher : Springer Science & Business Media
File Size : 31,6 Mb
Get Book
This book consists of three parts, rather different in level and purpose. The first part was origina...