Identifying robust features of community structure in complex networks

TL;DR

This paper introduces a recursive significance clustering method to identify stable, robust core communities in complex networks, addressing variability in community detection due to data uncertainty and network perturbations.

Contribution

The paper presents a novel recursive clustering scheme that detects stable core communities, improving reliability of community detection in uncertain and evolving networks.

Findings

Cores represent well-supported network features.

Cores are stable under network perturbations.

Cores remain cohesive over time.

Abstract

Network science has presented community detection as a valuable tool for revealing functional modules in complex systems rooted in the wiring architectures of complex networks. The varying procedures of community detection can produce, however, divisions of a network into communities that vary considerably in structure but are deemed to be of similar merit. This is especially problematic when the network is constructed on uncertain data, since small changes to the network's configuration can cause radically different structure to be detected. To reconcile with the ambiguity in interpreting degenerate network partitions as representations of the underlying system function, we introduce a recursive significance clustering scheme that identifies the subsets of nodes having stable joint community assignments under network perturbation. These robust node groups are referred to here as cores, and represent well-supported features of network structure as distinct from the nodes with unstable community assignments. We show that cores characterize the variability inherent to non-overlapping community structure in networks and are cohesive under temporal evolution of the network.