  We present Geo2DR (Geometric to Distributed Representations), a GPU ready
Python library for unsupervised learning on graph-structured data using
discrete substructure patterns and neural language models. It contains
efficient implementations of popular graph decomposition algorithms and neural
language models in PyTorch which can be combined to learn representations of
graphs using the distributive hypothesis. Furthermore, Geo2DR comes with
general data processing and loading methods to bring substantial speed-up in
the training of the neural language models. Through this we provide a modular
set of tools and methods to quickly construct systems capable of learning
distributed representations of graphs. This is useful for replication of
existing methods, modification, or development of completely new methods. This
paper serves to present the Geo2DR library and perform a comprehensive
comparative analysis of existing methods re-implemented using Geo2DR across
widely used graph classification benchmarks. Geo2DR displays a high
reproducibility of results in published methods and interoperability with other
libraries useful for distributive language modelling.
