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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Vydáno v:Scientific reports Ročník 15; číslo 1; s. 3788 - 15
Hlavní autoři: Hussain, Md Taufique, Halappanavar, Mahantesh, Chatterjee, Samrat, Radicchi, Filippo, Fortunato, Santo, Azad, Ariful
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 30.01.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/705/1046
639/705/117
Algorithms
Clustering
Community structure
Graphs
Humanities and Social Sciences
Methods
multidisciplinary
Optimization techniques
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
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Shrnutí: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