We present a novel model for classifying the quality of Wikipedia articles based on structural properties of a network representation of the article's revision history. We create revision history networks (an adaptation of Keegan et. al's article trajectory networks [7]), where nodes correspond to individual editors of an article, and edges join the authors of consecutive revisions. Using descriptive statistics generated from these networks, along with general properties like the number of edits and article size, we predict which of six quality classes (Start, Stub, C-Class, B-Class, Good, Featured) articles belong to, attaining a classification accuracy of 49.35% on a stratified sample of articles. These results suggest that structures of collaboration underlying the creation of articles, and not just the content of the article, should be considered for accurate quality classification.