# Little Ball of Fur: A Python Library for Graph Sampling

arXiv: https://arxiv.org/abs/2006.04311 · HTML: https://mlsanity.com/paper/2006.04311/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

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

- [Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings](https://mlsanity.com/paper/2101.03091/) (2021)
- [Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types](https://mlsanity.com/paper/2302.01313/) (2023)
- [Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models](https://mlsanity.com/paper/2409.19667/) (2024)
- [The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey](https://mlsanity.com/paper/2305.13854/) (2023)
- [A Survey on Graph Condensation](https://mlsanity.com/paper/2402.02000/) (2024)
- [Realistic Synthetic Social Networks with Graph Neural Networks](https://mlsanity.com/paper/2212.07843/) (2022)
- [Finding MNEMON: Reviving Memories of Node Embeddings](https://mlsanity.com/paper/2204.06963/) (2022)
- [Topology Only Pre-Training: Towards Generalised Multi-Domain Graph Models](https://mlsanity.com/paper/2311.03976/) (2023)
- [Sybil Detection using Graph Neural Networks](https://mlsanity.com/paper/2409.08631/) (2024)
- [Joint Combinatorial Node Selection and Resource Allocations in the Lightning Network using Attention-based Reinforcement Learning](https://mlsanity.com/paper/2411.17353/) (2024)
- [Diversity-aware Web APIs Recommendation with Compatibility Guarantee](https://mlsanity.com/paper/2108.04389/) (2021)
- [PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks](https://mlsanity.com/paper/2306.14357/) (2023)
- [HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2411.07022/) (2024)
- [Discrete Diffusion-Based Model-Level Explanation of Heterogeneous GNNs with Node Features](https://mlsanity.com/paper/2508.08458/) (2025)
- [Scalable Graph Coreset Selection via Greedy Sampling](https://mlsanity.com/paper/2607.27602/) (2026)

## Abstract (arXiv)

Sampling graphs is an important task in data mining. In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms. Our goal is to make node, edge, and exploration-based network sampling techniques accessible to a large number of professionals, researchers, and students in a single streamlined framework. We created this framework with a focus on a coherent application public interface which has a convenient design, generic input data requirements, and reasonable baseline settings of algorithms. Here we overview these design foundations of the framework in detail with illustrative code snippets. We show the practical usability of the library by estimating various global statistics of social networks and web graphs. Experiments demonstrate that Little Ball of Fur can speed up node and whole graph embedding techniques considerably with mildly deteriorating the predictive value of distilled features.