As we've seen, graphs collect and store complex, real-life interactions. This makes graphs robust data structures that are intuitive and flexible. Since graphs are non-euclidean data structures, they can't be used directly as input in a machine learning algorithm. This is why we need to learn graph embeddings in a low-dimensional space. We can perform different types of graph analytics tasks using embeddings. Embeddings are vector representations of a graph in an nn-dimensional space.

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