# Scikit-network: Graph Analysis in Python

arXiv: https://arxiv.org/abs/2009.07660 · HTML: https://mlsanity.com/paper/2009.07660/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 8 of 8)

- [Explanations of Black-Box Models based on Directional Feature Interactions](https://mlsanity.com/paper/2304.07670/) (2023)
- [Data-Driven Network Neuroscience: On Data Collection and Benchmark](https://mlsanity.com/paper/2211.12421/) (2022)
- [Partition and Code: learning how to compress graphs](https://mlsanity.com/paper/2107.01952/) (2021)
- [GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Tasks](https://mlsanity.com/paper/2608.01684/) (2026)
- [Incorporating Deep Learning Design in Database Queries](https://mlsanity.com/paper/2605.24207/) (2026)
- [Improving Knowledge Distillation with Teacher's Explanation](https://mlsanity.com/paper/2310.02572/) (2023)
- [PlasmoData.jl -- A Julia Framework for Modeling and Analyzing Complex Data as Graphs](https://mlsanity.com/paper/2401.11404/) (2024)
- [Neuro-Relational Programs: Unifying Queries and Neural Computation over Structured Data](https://mlsanity.com/paper/2606.11946/) (2026)

## Abstract (arXiv)

Scikit-network is a Python package inspired by scikit-learn for the analysis of large graphs. Graphs are represented by their adjacency matrix in the sparse CSR format of SciPy. The package provides state-of-the-art algorithms for ranking, clustering, classifying, embedding and visualizing the nodes of a graph. High performance is achieved through a mix of fast matrix-vector products (using SciPy), compiled code (using Cython) and parallel processing. The package is distributed under the BSD license, with dependencies limited to NumPy and SciPy. It is compatible with Python 3.6 and newer. Source code, documentation and installation instructions are available online.