Graph augmentation concerns the addition of edges to a graph to satisfy some connectivity property of a graph. Previous research in this field has been preoccupied with edge augmentation; however the research in this document focuses on the addition of vertices to a graph to satisfy a specific connectivity property: ensuring that all the nodes of the graph are contained within cycles. A distinction is made between graph augmentation (edge addition), and graph enlargement (vertex addition). This document expands on previous research into a graph matching problem known as the “shoe matching problem” and the role of a graph enlargement algorithm in finding this solution. The aim of this research was to develop new and efficient algorithms to solve the graph enlargement problem as applied to the shoe matching problem and to improve on the naïve algorithm of Sanders. Three new algorithms focusing on graph enlargement and the shoe matching problem are presented, with positive results overall. The new enlargement algorithms: cost-optimised, matrix, and subgraph, succeeded in deriving the best result (least number of total nodes required) in 37%, 53%, and 57% of cases respectively (measured across 120 cases). In contrast, Sanders’s algorithm has a success rate of only 20%; thus the new algorithms have a varying success rate of approximately 2 to 3 times that of Sanders’s algorithm. / Computing / M. Sc. Computing