Little Ball of Fur: A Python Library for Graph Sampling
Published in CIKM 2020, 2020
Recommended citation: B. Rozemberczki, O. Kiss and R. Sarkar. Little Ball of Fur: A Python Library for Graph Sampling, Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp 3133–3140, 2020 https://dl.acm.org/doi/abs/10.1145/3340531.3412758
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.
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Citing the paper:
>@inproceedings{10.1145/3340531.3412758,
author = {Rozemberczki, Benedek and Kiss, Oliver and Sarkar, Rik},
title = {Little Ball of Fur: A Python Library for Graph Sampling},
year = {2020},
isbn = {9781450368599},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3340531.3412758},
doi = {10.1145/3340531.3412758},
abstract = {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.},
booktitle = {Proceedings of the 29th ACM International Conference on Information & Knowledge Management},
pages = {3133–3140},
numpages = {8},
keywords = {network analysis, graph embedding, node embedding, graph mining, network embedding, graph analytics, network science, graph sampling},
location = {Virtual Event, Ireland},
series = {CIKM '20}
}