Estimating Network Parameters for Selecting Community Detection Algorithms

TL;DR

This paper investigates how to select appropriate community detection algorithms for networks by estimating network parameters, demonstrating that algorithm choice can be guided solely by observed network features.

Contribution

It introduces a method to estimate network parameters to inform the selection between weighted and unweighted community detection algorithms.

Findings

Algorithm performance varies across different network parameter regions.

Choice of algorithm can be predicted from network observations.

Guidelines for selecting community detection algorithms based on network properties.

Abstract

This paper considers the problem of algorithm selection for community detection. The aim of community detection is to identify sets of nodes in a network which are more interconnected relative to their connectivity to the rest of the network. A large number of algorithms have been developed to tackle this problem, but as with any machine learning task there is no "one-size-fits-all" and each algorithm excels in a specific part of the problem space. This paper examines the performance of algorithms developed for weighted networks against those using unweighted networks for different parts of the problem space (parameterised by the intra/inter community links). It is then demonstrated how the choice of algorithm (weighted/unweighted) can be made based only on the observed network.