Cluster-based network proximities for arbitrary nodal subsets.

ABSTRACT: The concept of a cluster or community in a network context has been of considerable interest in a variety of settings in recent years. In this paper, employing random walks and geodesic distance, we introduce a unified measure of cluster-based proximity between nodes, relative to a given subset of interest. The inherent simplicity and informativeness of the approach could make it of value to researchers in a variety of scientific fields. Applicability is demonstrated via application to clustering for a number of existent data sets (including multipartite networks). We view community detection (i.e. when the full set of network nodes is considered) as simply the limiting instance of clustering (for arbitrary subsets). This perspective should add to the dialogue on what constitutes a cluster

SUBMITTER: Berenhaut KS 

PROVIDER: S-EPMC6156331 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature