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A graph sampling extension library for NetworkX

A graph sampling extension library for NetworkX

Little Ball

Little Ball of Fur is a graph sampling extension library for NetworkX.

Please look at the Documentation and External Resources.

Little Ball of Fur consists of methods that can sample from graph structured data. To put it simply it is a Swiss Army knife for graph sampling tasks. First, it includes a large variety of vertex, edge, and exploration sampling techniques. Second, it provides a unified application public interface which makes the application of sampling algorithms trivial for end-users. Implemented methods cover a wide range of networking (Networking, INFOCOM, SIGCOMM) and data mining (KDD, TKDD, ICDE) conferences, workshops, and pieces from prominent journals.


Citing

If you find Little Ball of Fur and the new datasets useful in your research, please consider citing the following paper:

>@misc{littleballoffur2020,
       title={Little Ball of Fur: A Python Library for Graph Subsampling},
       author={Benedek Rozemberczki and Oliver Kiss},
       year={2020},
}

A simple example

Little Ball of Fur makes using modern graph subsampling techniques quite easy (see here for the accompanying tutorial).
For example, this is all it takes to use Random Walk Sampling on a Watts-Strogatz graph:

import networkx as nx
from littleballoffur import RandomWalkSampler

graph = nx.newman_watts_strogatz_graph(1000, 20, 0.05)

sampler = RandomWalkSampler()

new_graph = sampler.sample(graph)

GitHub

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