Graph Representation Learning (Paperback)

Hamilton, William L.

  • 出版商: Morgan & Claypool
  • 出版日期: 2020-09-16
  • 售價: $1,980
  • 貴賓價: 9.5$1,881
  • 語言: 英文
  • 頁數: 160
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1681739631
  • ISBN-13: 9781681739632
  • 相關分類: Machine Learning
  • 相關翻譯: 圖表示學習 (簡中版)
  • 立即出貨 (庫存=1)

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商品描述

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.

商品描述(中文翻譯)

圖結構化數據在自然科學和社會科學中無處不在,從電信網絡到量子化學。將關聯歸納偏差引入深度學習架構對於創建能夠從這種數據中學習、推理和泛化的系統至關重要。近年來,圖表示學習的研究激增,包括深度圖嵌入技術、將卷積神經網絡推廣到圖結構化數據的方法,以及受信念傳播啟發的神經消息傳遞方法。這些圖表示學習的進展在許多領域取得了新的最先進結果,包括化學合成、3D視覺、推薦系統、問答和社交網絡分析。

本書提供了圖表示學習的綜合和概述。首先討論了圖表示學習的目標,以及圖論和網絡分析的關鍵方法基礎。接著介紹和評論了學習節點嵌入的方法,包括基於隨機遊走的方法和應用於知識圖譜的方法。然後,提供了對高度成功的圖神經網絡(GNN)形式主義的技術綜合和介紹,該形式主義已成為深度學習圖數據的主導和快速增長的範式。本書最後綜合了圖表示學習中最近的深度生成模型的進展,這是圖表示學習的一個新興但快速增長的子集。