Then each row of the adjacency matrix A can be normalized (i.e., so that each row sums to 1), turning it into a right stochastic matrix S. Negative sampling, in the skip-gram model, uniformly draws at random for each training word one of its surrounding words as the (positive) target word.
this range is determined by the size of the training context c. a surrounding word w s of the training word w t must satisfy the following condition: For the same corpus, let us set d max equal to c in word2vec and generate a word co-occurrence network of the whole corpus.