Aditya Ballal

Loading metrics Open Access Peer-reviewed Research Article Abstract One of the goals of precision medicine is to classify patients into subgroups that differ in their susceptibility and response to a disease, thereby enabling tailored treatments for each subgroup. Therefore, there is a great need to identify distinctive clusters of patients from patient data.

Abstract We present an approach to partitioning network nodes into nonoverlapping communities, a key step in revealing network modularity and functional organization. Our methodology, applicable to networks with weighted or unweighted symmetric edges, uses random walks to explore neighboring nodes in the same community. The walk-likelihood algorithm (WLA) produces an optimal partition of network nodes into a given number of communities.