Learning distributed representations of graphs with Geo2DR

We present Geo2DR, 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 design methodology to quickly construct systems capable of learning distributed representations of graphs. This is useful for replication of existing methods, modification, or even creation of novel systems. This work 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. We show a high reproducibility of results in published methods and interoperability with other libraries useful for distributive language modelling.