Parallel median consensus clustering in complex networks

We develop an algorithm that finds the consensus among many different clustering solutions of a graph. We formulate the problem as a median set partitioning problem and propose a greedy optimization technique. Unlike other approaches that find median set partitions, our algorithm takes graph structu...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 3788 - 15
Main Authors: Hussain, Md Taufique, Halappanavar, Mahantesh, Chatterjee, Samrat, Radicchi, Filippo, Fortunato, Santo, Azad, Ariful
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 30.01.2025
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ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:We develop an algorithm that finds the consensus among many different clustering solutions of a graph. We formulate the problem as a median set partitioning problem and propose a greedy optimization technique. Unlike other approaches that find median set partitions, our algorithm takes graph structure into account and finds a comparable quality solution much faster than the other approaches. For graphs with known communities, our consensus partition captures the actual community structure more accurately than alternative approaches. To make it applicable to large graphs, we remove sequential dependencies from our algorithm and design a parallel algorithm. Our parallel algorithm achieves 35x speedup when utilizing 64 processing cores for large real-world graphs representing mass cytometry data from single-cell experiments.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-87479-6