管理员
发表于 2025-3-23 13:02:19
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.
Malcontent
发表于 2025-3-23 15:32:28
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蚀刻术
发表于 2025-3-23 21:52:56
978-3-031-16176-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
预兆好
发表于 2025-3-24 02:01:38
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Euphonious
发表于 2025-3-24 03:10:18
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ENNUI
发表于 2025-3-24 08:52:08
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
adroit
发表于 2025-3-24 10:48:28
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
Finasteride
发表于 2025-3-24 15:13:06
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
发表于 2025-3-24 21:45:16
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
发表于 2025-3-24 23:18:02
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