In both this paper and word2vec, the key concept is to try and represent a high-dimensional and sparse dataset as a dense and low-dimensional continuous vector. Interestingly, the same skip-gram algorithm is used for both even though it's applied to datasets as disparate as a social graph and a sentence structure. There's a bit of cleverness here: the authors equate a sequence of social network graph visits (a random walk in DeepWalk) to a sequence of words (a sentence in word2vec.) In both cases the resulting representation is dense while still preserving many relevant properties of a social group which makes it useful as an input to other ML algorithms. Incredibly interesting.<p>I wonder if there's a simple but powerful example (like king-man+woman=queen for word2vec) of this technique.