Little Ball of Fur: A Python Library for Graph Sampling
This provides a streamlined framework for professionals, researchers, and students to access various graph sampling techniques, but it is incremental as it packages existing methods.
The authors tackled the problem of graph sampling by introducing Little Ball of Fur, a Python library with over twenty algorithms, which speeds up node and whole graph embedding techniques with only mild deterioration in predictive value.
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.