Version 1
: Received: 23 August 2018 / Approved: 23 August 2018 / Online: 23 August 2018 (16:16:05 CEST)
How to cite:
Banf, M. Network Module Detection using Recursive Local Graph Sparsification and Clustering. Preprints2018, 2018080421. https://doi.org/10.20944/preprints201808.0421.v1
Banf, M. Network Module Detection using Recursive Local Graph Sparsification and Clustering. Preprints 2018, 2018080421. https://doi.org/10.20944/preprints201808.0421.v1
Banf, M. Network Module Detection using Recursive Local Graph Sparsification and Clustering. Preprints2018, 2018080421. https://doi.org/10.20944/preprints201808.0421.v1
APA Style
Banf, M. (2018). Network Module Detection using Recursive Local Graph Sparsification and Clustering. Preprints. https://doi.org/10.20944/preprints201808.0421.v1
Chicago/Turabian Style
Banf, M. 2018 "Network Module Detection using Recursive Local Graph Sparsification and Clustering" Preprints. https://doi.org/10.20944/preprints201808.0421.v1
Abstract
Here we present a fast and highly scalable community structure preserving network module detection that recursively integrates graph sparsification and clustering. Our algorithm, called SparseClust, participated in the most recent DREAM community challenge on disease module identification, an open competition to comprehensively assess module identification methods across a wide range of biological networks.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
