Little Ball of Fur: A Python Library for Graph Sampling

Benedek Rózemberczki, Oliver Kiss, Rik Sarkar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationProceedings of the 29th ACM International Conference on Information & Knowledge Management
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery, Inc
Number of pages8
ISBN (Print)9781450368599
Publication statusPublished - 19 Oct 2020
Event29th ACM International Conference on Information and Knowledge Management - Omline Conference
Duration: 19 Oct 202023 Oct 2020


Conference29th ACM International Conference on Information and Knowledge Management
Abbreviated titleCIKM 2020
CityOmline Conference
Internet address

Keywords / Materials (for Non-textual outputs)

  • graph analytics
  • node embedding
  • graph mining
  • network science
  • graph embedding
  • network embedding
  • network analysis
  • graph sampling


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