Article
Connecting the dots: The boons and banes of network modeling
Patterns
(2021)
Abstract
Network modeling transforms data into a structure of nodes and edges such that edges represent relationships between pairs of objects, then extracts clusters of densely connected nodes in order to capture high-dimensional relationships hidden in the data. This efficient and flexible strategy holds potential for unveiling complex patterns concealed within massive datasets, but standard implementations overlook several key issues that can undermine research efforts. These issues range from data imputation and discretization to correlation metrics, clustering methods, and validation of results. Here, we enumerate these pitfalls and provide practical strategies for alleviating their negative effects. These guidelines increase prospects for future research endeavors as they reduce type I and type II (false-positive and false-negative) errors and are generally applicable for network modeling applications across diverse domains.
Keywords
- network analysis,
- clustering,
- community detection,
- correlation,
- high-dimensional patterns,
- gene co-expression analysis
Disciplines
Publication Date
December, 2021
DOI
10.1016/j.patter.2021.100374
Citation Information
Sharlee Climer. "Connecting the dots: The boons and banes of network modeling" Patterns Vol. 2 Iss. 12 (2021) Available at: http://works.bepress.com/sharlee-climer/33/