Introduction to Graph Neural Networks

Liu, Zhiyuan, Zhou, Jie

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商品描述

Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool.

This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.

商品描述(中文翻譯)

圖形是在複雜的現實應用中有用的數據結構,例如建模物理系統、學習分子指紋、控制交通網絡和在社交網絡中推薦朋友。然而,這些任務需要處理包含元素之間豐富關係信息的非歐幾里德圖形數據,傳統的深度學習模型(例如卷積神經網絡(CNN)或循環神經網絡(RNN))無法很好地處理。圖形中的節點通常包含有用的特徵信息,在大多數無監督表示學習方法(例如網絡嵌入方法)中無法很好地解決。圖形神經網絡(GNN)被提出來將特徵信息和圖形結構相結合,通過特徵傳播和聚合在圖形上學習更好的表示。由於其令人信服的性能和高可解釋性,GNN最近已成為廣泛應用的圖形分析工具。

本書全面介紹了圖形神經網絡的基本概念、模型和應用。首先介紹了基本的GNN模型。然後介紹了幾種變體,如圖形卷積網絡、圖形循環網絡、圖形注意網絡、圖形殘差網絡和幾個通用框架。還包括了不同圖形類型的變體和高級訓練方法。至於GNN的應用,本書將其分為結構、非結構和其他場景,然後介紹了解決這些任務的幾個典型模型。最後,結束章節提供了GNN的開放資源和幾個未來方向的展望。