Connecting the dots: The boons and banes of network modeling

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.