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Background and Traditional Approaches,e will provide a very brief and focused tour of traditional learning approaches over graphs, providing pointers and references to more thorough treatments of these methodological approaches along the way. This background chapter will also serve to introduce key concepts from graph analysis that will form the foundation for later chapters.

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Conclusion, 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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Deep Generative ModelsHowever, 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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Theoretical Motivationsches— which form the basis of most modern GNNs—were proposed by analogy to message passing algorithms for probabilistic inference in graphical models [Dai et al., 2016]. And lastly, GNNs have been motivated in several works based on their connection to the Weisfeiler-Lehman graph isomorphism test [Hamilton et al., 2017b].

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