[D] Why is the skip-gram model used in DeepWalk and Node2vec rather than CBOW?

I am curious to understand the reason behind the decision to use Skip-gram rather than CBOW for these two models. According to the original Word2vec paper, CBOW is faster to train and captures syntactic similarities better whereas the skip-gram is slower at training but captures more robust semantic similarities and is also better at handling infrequent words. How does this apply to graph theory and what motivated this decision?


DeepWalk: [https://dl.acm.org/doi/abs/10.1145/2623330.2623732](https://dl.acm.org/doi/abs/10.1145/2623330.2623732)

Node2vec: [https://dl.acm.org/doi/abs/10.1145/2939672.2939754](https://dl.acm.org/doi/abs/10.1145/2939672.2939754)

deepwalk machinelearning node2vec