# Learning distributed representations of graphs with Geo2DR

arXiv: https://arxiv.org/abs/2003.05926 · HTML: https://mlsanity.com/paper/2003.05926/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models](https://mlsanity.com/paper/2104.07788/) (2021)
- [Pattern Analysis of Money Flow in the Bitcoin Blockchain](https://mlsanity.com/paper/2207.07315/) (2022)
- [Distributed representations of graphs for drug pair scoring](https://mlsanity.com/paper/2209.09383/) (2022)

## Abstract (arXiv)

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.