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 language | English |
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Title of host publication | CIKM 2020: Proceedings of the 29th ACM International Conference on Information and Knowledge Management |
Editors | Mathieu d'Aquin, Stefan Dietze |
Publisher | Association for Computing Machinery |
Pages | 3133-3140 |
Number of pages | 8 |
ISBN (Electronic) | 9781450368599 |
DOIs | |
State | Published - 19 Oct 2020 |
Event | 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland Duration: 19 Oct 2020 → 23 Oct 2020 |
Publication series
Name | International Conference on Information and Knowledge Management, Proceedings |
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Conference
Conference | 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 |
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Country/Territory | Ireland |
City | Virtual, Online |
Period | 19/10/20 → 23/10/20 |
Keywords
- graph analytics
- graph embedding
- graph mining
- graph sampling
- network analysis
- network embedding
- network science
- node embedding