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Graph Neural Networks in Practicenctions and regularization are generally used. In this chapter, we will turn our attention to some of these practical aspects of GNNs. We will discuss some representative applications and how GNNs are generally optimized in practice, including a discussion of unsupervised pre-training methods that c

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Theoretical Motivationsly 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

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Deep Generative Modelsnthetic 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

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https://doi.org/10.1007/978-3-658-41287-6 a graph we might use nodes to represent individuals and use edges to represent that two individuals are friends (Figure 1.1). In the biological domain we could use the nodes in a graph to represent proteins, and use the edges to represent various biological interactions, such as kinetic interactions between proteins.

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https://doi.org/10.1007/978-3-322-83428-7 hope is that these chapters provide a sufficient foundation and overview for those who are interested in becoming practitioners of these techniques or those who are seeking to explore new methodological frontiers of this area.

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https://doi.org/10.1007/978-3-658-38200-1apter, we turn our focus to more complex encoder models. We will introduce the . formalism, which is a general framework for defining deep neural networks on graph data. The key idea is that we want to generate representations of nodes that actually depend on the structure of the graph, as well as any feature information we might have.

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https://doi.org/10.1007/978-3-8349-8115-8However, a key limitation of those traditional approaches is that they rely on a fixed, hand-crafted generation process. In short, the traditional approaches can generate graphs, but they lack the ability to . a generative model from data.

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