Standard word embedding algorithms, such as word2vec and Glove, make a restrictive assumption that words are likely to be semantically related only if they co-occur locally within a window of fixed size. However, this restrictive assumption may not capture the semantic association between words that co-occur frequently but non-locally within documents. To alleviate this restriction, in this paper, we propose a graph-based word embedding method, named 'word-node2vec'. By relaxing the strong constraint of locality, our method is able to capture both local and non-local co-occurrences. Wordnode2vec constructs a weighted graph, where each node represents a word and the weight of an edge between two nodes represents a combination of both local (e.g. word2vec) and document-level co-occurrences. Our experiments show that word-node2vec outperforms word2vec and glove on a range of different tasks, such as word-pair similarity prediction, word analogy and concept categorization.