This is an implementation of the Chinese Whispers clustering algorithm in Python. Since this library is based on NetworkX, it is simple to use.
Given a NetworkX graph G, this library can cluster it using the following code:
from chinese_whispers import chinese_whispers
chinese_whispers(G, weighting='top', iterations=20)As the result, each node of the input graph is provided with the label attribute that stores the cluster label.
The library also offers a convenient command-line interface (CLI) for clustering graphs represented in the ABC tab-separated format (source\ttarget\tweight).
# Write karate_club.tsv (just as example)
python3 -c 'import networkx as nx; nx.write_weighted_edgelist(nx.karate_club_graph(), "karate_club.tsv", delimiter="\t")'
# Using as CLI
chinese-whispers karate_club.tsv
# Using as module (same CLI as above)
python3 -mchinese_whispers karate_club.tsvA more complete usage example is available in the example notebook and at https://nlpub.github.io/chinese-whispers/.
In case you require higher performance, please consider our Java implementation that also includes other graph clustering algorithms: https://github.com/nlpub/watset-java.
- Ustalov, D., Panchenko, A., Biemann, C., Ponzetto, S.P.: Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction. Computational Linguistics 45(3), 423–479 (2019)
@article{Ustalov:19:cl,
author = {Ustalov, Dmitry and Panchenko, Alexander and Biemann, Chris and Ponzetto, Simone Paolo},
title = {{Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction}},
journal = {Computational Linguistics},
year = {2019},
volume = {45},
number = {3},
pages = {423--479},
doi = {10.1162/COLI_a_00354},
publisher = {MIT Press},
issn = {0891-2017},
language = {english},
}Copyright (c) 2018–2024 Dmitry Ustalov. See LICENSE for details.