Titlebook: ;

笨拙處理

靦腆

hauteur

魔鬼在游行

https://doi.org/10.1007/978-3-642-41289-9ly developed from distinct theoretical motivations. From one perspective, GNNs were developed based on the theory of graph signal processing, as a generalization of Euclidean convolutions to the non-Euclidean graph domain [Bruna et al., 2014]. At the same time, however, neural message passing approa

Landlocked

https://doi.org/10.1007/978-3-8349-8115-8nthetic graphs that have certain properties, and they can be used to give us insight into how certain graph structures might arise in the real world. However, a key limitation of those traditional approaches is that they rely on a fixed, hand-crafted generation process. In short, the traditional app

consolidate

濕潤

Background and Traditional Approaches,and context. What kinds of methods were used for machine learning on graphs prior to the advent of modern deep learning approaches? In this chapter, we will provide a very brief and focused tour of traditional learning approaches over graphs, providing pointers and references to more thorough treatm

主動

surrogate

Neighborhood Reconstruction Methodstheir graph position and the structure of their local graph neighborhood. In other words, we want to project nodes into a latent space, where geometric relations in this latent space correspond to relationships (e.g., edges) in the original graph or network [Hoff et al., 2002] (Figure 3.1).

faddish

The Graph Neural Network Modelcussed used a . embedding approach to generate representations of nodes, where we simply optimized a unique embedding vector for each node. In this chapter, we turn our focus to more complex encoder models. We will introduce the . formalism, which is a general framework for defining deep neural netw