A Newtonian Framework for Community Detection in Undirected Biological Networks

Community detection is a key problem of interest in network analysis, with applications in a variety of domains such as biological networks, social network modeling, and communication pattern analysis. In this paper, we present a novel framework for community detection that is motivated by a physical system analogy. We model a network as a system of point masses, and drive the process of community detection, by leveraging the Newtonian interactions between the point masses. Our framework is designed to be generic and extensible relative to the model parameters that are most suited for the problem domain. We illustrate the applicability of our approach by applying the Newtonian Community Detection algorithm on protein-protein interaction networks of E. coli , C. elegans, and S. cerevisiae. We obtain results that are comparable in quality to those obtained from the Newman-Girvan algorithm, a widely employed divisive algorithm for community detection. We also present a detailed analysis of the structural properties of the communities produced by our proposed algorithm, together with a biological interpretation using E. coli protein network as a case study. A functional enrichment heat map is constructed with the Gene Ontology functional mapping, in addition to a pathway analysis for each community. The analysis illustrates that the proposed algorithm elicits communities that are not only meaningful from a topological standpoint, but also possess biological relevance. We believe that our algorithm has the potential to serve as a key computational tool for driving therapeutic applications involving targeted drug development for personalized care delivery.