  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.
