The manner in which keywords fulfill the role of being central to a document is frustratingly still an open question. In this paper, we hope to shed some light on the essence of keywords in scientific articles and thereby motivate the graph-based approach to keyword extraction. We identify the document model captured by the text graph generated as input to a number of centrality metrics, and overview what these metrics say about keywords. In doing so, we achieve state-of-the-art results in unsupervised non-contextual single document keyword extraction.