Node Dominance: Revealing Community and Core-Periphery Structure in Social Networks

TL;DR

This paper introduces a node dominance-based method for revealing core-periphery and community structures in social networks, using iterative node removal to identify essential nodes and peripheral communities, validated on real-world data.

Contribution

It presents a novel distributed algorithm leveraging node dominance for core-periphery decomposition and community detection in social networks.

Findings

Core nodes are crucial for network flow and structure.

Peripheral components reveal community organization.

Method validated on DBLP co-authorship network.

Abstract

This study relates the local property of node dominance to local and global properties of a network. Iterative removal of dominated nodes yields a distributed algorithm for computing a core-periphery decomposition of a social network, where nodes in the network core are seen to be essential in terms of network flow and global structure. Additionally, the connected components in the periphery give information about the community structure of the network, aiding in community detection. A number of explicit results are derived, relating the core and periphery to network flow, community structure and global network structure, which are corroborated by observational results. The method is illustrated using a real world network (DBLP co-authorship network), with ground-truth communities.