# GraSPy: Graph Statistics in Python

arXiv: https://arxiv.org/abs/1904.05329 · HTML: https://mlsanity.com/paper/1904.05329/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

## Built on by (most cited first, 15 of 15)

- [Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models](https://mlsanity.com/paper/2409.19667/) (2024)
- [Tracking the perspectives of interacting language models](https://mlsanity.com/paper/2406.11938/) (2024)
- [Consistent estimation of generative model representations in the data kernel perspective space](https://mlsanity.com/paper/2409.17308/) (2024)
- [Statistical inference on black-box generative models in the data kernel perspective space](https://mlsanity.com/paper/2410.01106/) (2024)
- [Graph Matching via Optimal Transport](https://mlsanity.com/paper/2111.05366/) (2021)
- [Online Change Point Detection for Weighted and Directed Random Dot Product Graphs](https://mlsanity.com/paper/2201.11222/) (2022)
- [Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport](https://mlsanity.com/paper/2210.14378/) (2022)
- [Inducing a hierarchy for multi-class classification problems](https://mlsanity.com/paper/2102.10263/) (2021)
- [Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery](https://mlsanity.com/paper/2405.12797/) (2024)
- [Gradient-Based Spectral Embeddings of Random Dot Product Graphs](https://mlsanity.com/paper/2307.13818/) (2023)
- [Leveraging semantically similar queries for ranking via combining representations](https://mlsanity.com/paper/2106.12621/) (2021)
- [An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces](https://mlsanity.com/paper/2109.12640/) (2021)
- [Principal Graph Encoder Embedding and Principal Community Detection](https://mlsanity.com/paper/2501.14939/) (2025)
- [LASE: Learned Adjacency Spectral Embeddings](https://mlsanity.com/paper/2412.17734/) (2024)
- [The ASE-LSE Disagreement Landscape: An End-to-End Characterisation of Extremes and Structural Drivers](https://mlsanity.com/paper/2605.22346/) (2026)

## Abstract (arXiv)

We introduce GraSPy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations. This package provides flexible and easy-to-use algorithms for analyzing and understanding graphs with a scikit-learn compliant API. GraSPy can be downloaded from Python Package Index (PyPi), and is released under the Apache 2.0 open-source license. The documentation and all releases are available at https://neurodata.io/graspy.