Graph neural networks: Variations and applications

Опубликовано 21 апреля 2018, 4:02

Many real-world tasks require understanding interactions between a set of entities. Examples include interacting atoms in chemical molecules, people in social networks and even syntactic interactions between tokens in program source code. Graph structured data types are a natural representation for such systems, and several architectures have been proposed for applying deep learning methods to these structured objects. I will give an overview of the research directions inside Microsoft that have explored different architectures and applications for deep learning on graph structured data.

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