The difficulty of the frequent subgraph mining problem arises from the tasks of enumerating the subgraphs and calculating their support in the dataset. If the dataset graphs have additional information in the form of labels, these problems can be solved quite easily. However, if the dataset graphs are unlabeled or only have a few labels, then the complexity of these problems greatly reduces the number and sizes of the dataset graphs that can be managed. Thus far, researchers working on the frequent subgraph mining problem have given little attention to such datasets, and current algorithms tend to do poorly on them. Yet, there are many applications which deal with this type of data, mainly in the fields of compute vision where the data is structured as 2D or 3D meshes [8], or communication/transportation networks where the information is mostly topological.