This paper presents a novel framework for multi-folder email classification using graph mining as the underlying technique. Although several techniques exist (e.g., SVM, TF-IDF, n-gram) for addressing this problem in a delimited context, they heavily rely on extracting high-frequency keywords, thus ignoring the inherent structural aspects of an email (or document in general) which can play a critical role in classification. Some of the models (e.g., n-gram) consider only the words without taking into consideration where in the structure these words appear together. This paper presents a supervised learning model that leverages graph mining techniques for multi-folder email classification. A ranking formula is presented for ordering the representative - common and recurring - substructures generated from pre-classified emails. These ranked representative substructures are then used for categorizing incoming emails. This approach is based on a global ranking model that incorporates several relevant parameters for email classification and overcomes numerous problems faced by extant approaches used for multi-folder classification. A number of parameters which influence the generation of representative substructures are analyzed, reexamined, and adapted to multiple folders. The effect of graph representations has been analyzed. The effectiveness of the proposed approach has been validated experimentally.