On the use of local structural properties for improving the efficiency of hierarchical community detection methods

TL;DR

This paper explores how local structural properties can serve as proxies to enhance the efficiency of hierarchical community detection in large networks, while maintaining competitive modularity results.

Contribution

It introduces the use of local link prediction features and network pruning heuristics to improve hierarchical community detection efficiency.

Findings

Local structural properties can effectively replace global metrics.

Network pruning reduces computational cost significantly.

Competitive modularity scores are achieved with the proposed methods.

Abstract

Community detection is a fundamental problem in the analysis of complex networks. It is the analogue of clustering in network data mining. Within community detection methods, hierarchical algorithms are popular. However, their iterative nature and the need to recompute the structural properties used to split the network (i.e. edge betweenness in Girvan and Newman's algorithm), make them unsuitable for large network data sets. In this paper, we study how local structural network properties can be used as proxies to improve the efficiency of hierarchical community detection while, at the same time, achieving competitive results in terms of modularity. In particular, we study the potential use of the structural properties commonly used to perform local link prediction, a supervised learning problem where community structure is relevant, as nodes are prone to establish new links with other nodes within their communities. In addition, we check the performance impact of network pruning heuristics as an ancillary tactic to make hierarchical community detection more efficient