Note: Distance-Based Network Partitioning

TL;DR

This paper introduces a distance-based approach for detecting soft communities in networks by using reference nodes and multidimensional distance vectors, effectively revealing community structures at multiple scales.

Contribution

It presents a novel method that leverages reference nodes and distance vectors to identify communities, including soft or overlapping ones, across various network scales.

Findings

Effective in detecting communities in spatial network models

Successfully applied to Zachary's karate club network

Reveals community structure through well-separated clusters

Abstract

A new method for identifying soft communities in networks is proposed. Reference nodes, either selected using a priori information about the network or according to relevant node measurements, are obtained. Distance vectors between each network node and the reference nodes are then used for defining a multidimensional coordinate system representing the community structure of the network at many different scales. For modular networks, the distribution of nodes in this space often results in a well-separated clustered structure, with each cluster corresponding to a community. The potential of the method is illustrated with respect to a spatial network model and the Zachary's karate club network.