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21 January 2019

In the last couple of years deep learning (DL) has become a main enabler for applications in many domains such as vision, NLP, audio, click stream data etc. Recently researchers started to successfully apply deep learning methods to graph datasets in domains like social networks, recommender systems and biology, where data is inherently structured in a graphical way. So how do Graph Neural Networks work? Why do we need them?   The Premise of Deep Learning In machine learning tasks involving graphical data, we usually want to describe each node in the graph in a way that allows us to feed it into some machine learning algorithm. Without DL, one would have to manually extract features, such as the number of neighbors a node has. But this is a laborious job. This is where DL shines. It automatically exploits the structure of the graph in order to extract features for […]

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