Summary
In this chapter, we have learned how unsupervised machine learning can be effectively applied to graphs to solve real problems, such as node and graph representation learning.
In particular, we first analyzed shallow embedding methods, a set of algorithms that are able to learn and return only the embedding values for the learned input data.
We then learned how autoencoder algorithms can be used to encode the input by preserving important information in a lower-dimensional space. We have also seen how this idea can be adapted to graphs, by learning about embeddings that allow us to reconstruct the pair-wise node/graph similarity.
Finally, we introduced the main concepts behind GNNs. We have seen how well-known concepts, such as convolution, can be applied to graphs.
In the next chapter, we will revise these concepts in a supervised setting. There, a target label is provided and the objective is to learn a mapping between the input and the output.
