Preprint Article Version 1 This version is not peer-reviewed

Network Module Detection using Recursive Local Graph Sparsification and Clustering

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. Preprints 2018, 2018080421 (doi: 10.20944/preprints201808.0421.v1). Banf, M. Network Module Detection using Recursive Local Graph Sparsification and Clustering. Preprints 2018, 2018080421 (doi: 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.

Subject Areas

Graph clustering, Unsupervised structure learning, Network module inference

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