Little Ball of Fur: A Python Library for Graph Sampling

Benedek Rozemberczki, Oliver Kiss, Rik Sarkar

    Research output: Contribution to Book/Report typesConference contributionpeer-review

    Abstract (may include machine translation)

    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 publicationCIKM 2020: Proceedings of the 29th ACM International Conference on Information and Knowledge Management
    EditorsMathieu d'Aquin, Stefan Dietze
    PublisherAssociation for Computing Machinery
    Pages3133-3140
    Number of pages8
    ISBN (Electronic)9781450368599
    DOIs
    StatePublished - 19 Oct 2020
    Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
    Duration: 19 Oct 202023 Oct 2020

    Publication series

    NameInternational Conference on Information and Knowledge Management, Proceedings

    Conference

    Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
    Country/TerritoryIreland
    CityVirtual, Online
    Period19/10/2023/10/20

    Keywords

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

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