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Little Ball of Fur is a graph sampling extension library for Python.

Please look at the Documentation, relevant Paper, Promo video and External Resources.

Little Ball of Fur consists of methods that can sample from graph structured data. To put it simply it is a Swiss Army knife for graph sampling tasks. First, it includes a large variety of vertex, edge, and exploration sampling techniques. Second, it provides a unified application public interface which makes the application of sampling algorithms trivial for end-users. Implemented methods cover a wide range of networking (Networking, INFOCOM, SIGCOMM) and data mining (KDD, TKDD, ICDE) conferences, workshops, and pieces from prominent journals.


If you find Little Ball of Fur useful in your research, please consider citing the following paper:

               title={{Little Ball of Fur: A Python Library for Graph Sampling}},
               author={Benedek Rozemberczki and Oliver Kiss and Rik Sarkar},
               pages = {3133–3140},
               booktitle={Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20)},

A simple example

Little Ball of Fur makes using modern graph subsampling techniques quite easy (see here for the accompanying tutorial). For example, this is all it takes to use Diffusion Sampling on a Watts-Strogatz graph:

import networkx as nx
from littleballoffur import DiffusionSampler

graph = nx.newman_watts_strogatz_graph(1000, 20, 0.05)

sampler = DiffusionSampler()

new_graph = sampler.sample(graph)

Methods included

In detail, the following sampling methods were implemented.

Node Sampling

Edge Sampling

Exploration Based Sampling

Expand to see all exploration samplers...

Head over to our documentation to find out more about installation and data handling, a full list of implemented methods, and datasets. For a quick start, check out our examples.

If you notice anything unexpected, please open an issue and let us know. If you are missing a specific method, feel free to open a feature request. We are motivated to constantly make Little Ball of Fur even better.


Little Ball of Fur can be installed with the following pip command.

$ pip install littleballoffur

As we create new releases frequently, upgrading the package casually might be beneficial.

$ pip install littleballoffur --upgrade

Running examples

As part of the documentation we provide a number of use cases to show how to use various sampling techniques. These can accessed here with detailed explanations.

Besides the case studies we provide synthetic examples for each model. These can be tried out by running the scripts in the examples folder. You can try out the random walk sampling example by running:

$ cd examples
$ python ./exploration_sampling/

Running tests

$ python test