4.2 Article

A framework for stability-based module detection in correlation graphs

Journal

STATISTICAL ANALYSIS AND DATA MINING
Volume 14, Issue 2, Pages 129-143

Publisher

WILEY
DOI: 10.1002/sam.11495

Keywords

clustering; graphical model; Jaccard coefficient; module detection; network; stability

Funding

  1. National Cancer Institute [P30CA016056, U24CA232979]
  2. National Institute of Environmental Health Sciences [R01ES018846, R21ES026429]

Ask authors/readers for more resources

Graphs are used to represent relationships between variables, and detecting structure within a graph is a challenging problem. This study addresses the issue of uncertainty in module detection by utilizing a nonparametric bootstrap approach to assess stability in a graph. The results show that this method can optimize stability in module detection within a graph.
Graphs can be used to represent the direct and indirect relationships between variables, and elucidate complex relationships and interdependencies. Detecting structure within a graph is a challenging problem. This problem is studied over a range of fields and is sometimes termed community detection, module detection, or graph partitioning. A popular class of algorithms for module detection relies on optimizing a function of modularity to identify the structure. In practice, graphs are often learned from the data, and thus prone to uncertainty. In these settings, the uncertainty of the network structure can become exaggerated by giving unreliable estimates of the module structure. In this work, we begin to address this challenge through the use of a nonparametric bootstrap approach to assessing the stability of module detection in a graph. Estimates of stability are presented at the level of the individual node, the inferred modules, and as an overall measure of performance for module detection in a given graph. Furthermore, bootstrap stability estimates are derived for complexity parameter selection that ultimately defines a graph from data in a way that optimizes stability. This approach is utilized in connection with correlation graphs but is generalizable to other graphs that are defined through the use of dissimilarity measures. We demonstrate our approach using a broad range of simulations and on a metabolomics dataset from the Beijing Olympics Air Pollution study. These approaches are implemented using bootcluster package that is available in the R programming language.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.2
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available