Titlebook: Advances in Graph Neural Networks; Chuan Shi,Xiao Wang,Cheng Yang Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusi

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Making Sense of the Smell of Bangladeshduce some basic concepts and definitions in graph representation learning, and discuss the development of the advanced graph representation learning methods, i.e., graph neural networks. We also emphasize several frontier aspects of graph neural networks mentioned in the book and further conclude the organization of the book in this chapter.

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978-3-031-16176-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Euphonious

ENNUI

Making Sense of the Smell of BangladeshGraphs or networks are usually used to model relational structures. And researches of graphs have attracted extensive attentions recently, the most important of which is graph representation learning, i.e., learning node embedding representations for downstream tasks. In this chapter, we first intro

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Making Sense of the Smell of Bangladesho two categories, spectral based (from the perspective of graph signal processing) and spatial based (from the perspective of information propagation). Since Graph Convolution Network (GCN) bridges the gap between them, spatial-based methods have developed rapidly recently due to their efficiency an

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Palgrave Studies in the History of Childhoode-passing rule that aggregates the information of neighbors to update node representations. The design of message-passing function is the most fundamental part of GNNs. In this chapter, we will introduce the message-passing functions of three representative homogeneous GNNs. Further, we show that mo

粘

Palgrave Studies in the History of Childhooderable research interest. Recently, some works attempt to generalize them to heterogeneous graphs which contain different types of nodes and relations. In this chapter, we introduce three heterogeneous graph neural networks (HGNNs), including heterogeneous graph propagation network (hpn), distance e

BUMP

https://doi.org/10.1057/9781137364500rld, complex systems are commonly associated with multiple temporal interactions, forming the so-called dynamic graphs. In this chapter, we will introduce three dynamic graph neural networks for temporal modeling of evolving structures, including simple homogeneous topologies and temporal heterogene