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Two provably consistent divide-and-conquer clustering algorithms for large networks
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DOI：10.1073/pnas.2100482118.png)
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In this article, we advance divide-and-conquer strategies for solv -ing the community detection problem in networks. We propose two algorithms that perform clustering on several small sub-graphs and finally patch the results into a single clustering. The main advantage of these algorithms is that they significantly bring down the computational cost of traditional algorithms, including spectral clustering, semidefinite programs, modularity-based methods, likelihood-based methods, etc., without losing accuracy, and even improving accuracy at times. These algorithms are also, by nature, parallelizable. Since most traditional algo-rithms are accurate, and the corresponding optimization prob-lems are much simpler in small problems, our divide-and-conquer methods provide an omnibus recipe for scaling traditional algo-rithms up to large networks. We prove the consistency of these algorithms under various subgraph selection procedures and per -form extensive simulations and real-data analysis to understand the advantages of the divide-and-conquer approach in various settings.
Keyword:
networks
divide-and-conquer
clustering
期刊
P
IF:
9.1
论文数:
10.8W
被引数:
73.5W
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