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I want to extract global features for input graphs. I thought about using node2vec to build embeddings for each node of a graph. How can I use these node embeddings to come up with global features for the graphs? Does averaging the embeddings of all the nodes in a graph and considering the resulting vector the feature vector of the graph seem appropriate?

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The embedding themselves, are learned in the same way as word2vec’s embeddings are learned using a skip-gram model.

If you post which explains it in great detail as from this point forward I assume you are familiar with it.are familiar with the word2vec skip-gram model, great, if not I recommend this great

The most natural way I can think about explaining node2vec is to explain how node2vec generates a “corpus” and if we understand word2vec we already know how to embed a corpus.

So how do we produce this corpus from a graph? That’s exactly the innovative part of node2vec and it does so in an intelligent way which is done using the sampling strategy.

For more information regarding the same, refer to the following link: https://towardsdatascience.com/node2vec-embeddings-for-graph-data-32a866340fef

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