Communities and bottlenecks: Trees and treelike networks have high modularity

TL;DR

This paper reveals that trees and treelike networks can exhibit unexpectedly high modularity scores, challenging assumptions about their community structure and impacting how modularity-based methods are interpreted.

Contribution

It demonstrates that trees can have high modularity values and analyzes the implications for community detection methods, highlighting potential pitfalls.

Findings

Trees can have arbitrarily high modularity scores.

Community detection in trees often yields significant modularity.

High modularity communities in trees are statistically significant.

Abstract

Much effort has gone into understanding the modular nature of complex networks. Communities, also known as clusters or modules, are typically considered to be densely interconnected groups of nodes that are only sparsely connected to other groups in the network. Discovering high quality communities is a difficult and important problem in a number of areas. The most popular approach is the objective function known as modularity, used both to discover communities and to measure their strength. To understand the modular structure of networks it is then crucial to know how such functions evaluate different topologies, what features they account for, and what implicit assumptions they may make. We show that trees and treelike networks can have unexpectedly and often arbitrarily high values of modularity. This is surprising since trees are maximally sparse connected graphs and are not typically considered to possess modular structure, yet the nonlocal null model used by modularity assigns low probabilities, and thus high significance, to the densities of these sparse tree communities. We further study the practical performance of popular methods on model trees and on a genealogical data set and find that the discovered communities also have very high modularity, often approaching its maximum value. Statistical tests reveal the communities in trees to be significant, in contrast with known results for partitions of sparse, random graphs.