  Analyzing the writing styles of authors and articles is a key to supporting
various literary analyses such as author attribution and genre detection. Over
the years, rich sets of features that include stylometry, bag-of-words, n-grams
have been widely used to perform such analysis. However, the effectiveness of
these features largely depends on the linguistic aspects of a particular
language and datasets specific characteristics. Consequently, techniques based
on these feature sets cannot give desired results across domains. In this
paper, we propose a novel Word2vec graph based modeling of a document that can
rightly capture both context and style of the document. By using these Word2vec
graph based features, we perform classification to perform author attribution
and genre detection tasks. Our detailed experimental study with a comprehensive
set of literary writings shows the effectiveness of this method over
traditional feature based approaches. Our code and data are publicly available
at https://cutt.ly/svLjSgk
