We present Geo2DR (Geometric to Distributed Representations), a 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 are combined to learn representations using the distributive hypothesis. Furthermore, Geo2DR comes with general data processing and loading methods which can bring substantial speed-up in the training of the neural language models. Through this we provide a unified 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 novel systems. This paper serves to present the Geo2DR library and perform a comprehensive comparative analysis of existing methods re-implemented using Geo2DR across several 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.

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