Negative sampling is an important component in word2vec for distributed word representation learning. We hypothesize that taking into account global, corpuslevel information and generating a different noise distribution for each target word better satisfies the requirements of negative examples for each training word than the original frequency-based distribution. In this purpose we pre-compute word cooccurrence statistics from the corpus and apply to it network algorithms such as random walk. We test this hypothesis through a set of experiments whose results show that our approach boosts the word analogy task by about 5\% and improves the performance on word similarity tasks by about 1\% compared to the skip-gram negative sampling baseline.