Benchmark model to assess community structure in evolving networks

TL;DR

This paper introduces a benchmark model based on stochastic block models to evaluate community detection methods in evolving networks, emphasizing the importance of capturing temporal dynamics.

Contribution

It presents a novel dynamic benchmark graph generator and extends quality indices for assessing community detection in evolving networks.

Findings

The benchmark models oscillate between community structures to simulate evolution.

Extended indices enable better comparison of dynamic community detection methods.

Provides a tool for evaluating temporal community detection accuracy.

Abstract

Detecting the time evolution of the community structure of networks is crucial to identify major changes in the internal organization of many complex systems, which may undergo important endogenous or exogenous events. This analysis can be done in two ways: considering each snapshot as an independent community detection problem or taking into account the whole evolution of the network. In the first case, one can apply static methods on the temporal snapshots, which correspond to configurations of the system in short time windows, and match afterwards the communities across layers. Alternatively, one can develop dedicated dynamic procedures, so that multiple snapshots are simultaneously taken into account while detecting communities, which allows us to keep memory of the flow. To check how well a method of any kind could capture the evolution of communities, suitable benchmarks are needed. Here we propose a model for generating simple dynamic benchmark graphs, based on stochastic block models. In them, the time evolution consists of a periodic oscillation of the system's structure between configurations with built-in community structure. We also propose the extension of quality comparison indices to the dynamic scenario.