Optimal timescale for community detection in growing networks

TL;DR

This paper investigates how choosing the right observation timescale, based on the system's intrinsic aging timescale, improves community detection in growing networks, revealing more accurate structural insights.

Contribution

It introduces a method to determine the optimal timescale for community detection in evolving networks using a multi-layer quality function, linking it to the network's aging process.

Findings

Optimal timescale aligns with the system's intrinsic aging timescale.

Temporal information significantly alters community detection results.

Uncovering timescales is crucial for understanding network structure.

Abstract

Time-stamped data are increasingly available for many social, economic, and information systems that can be represented as networks growing with time. The World Wide Web, social contact networks, and citation networks of scientific papers and online news articles, for example, are of this kind. Static methods can be inadequate for the analysis of growing networks as they miss essential information on the system's dynamics. At the same time, time-aware methods require the choice of an observation timescale, yet we lack principled ways to determine it. We focus on the popular community detection problem which aims to partition a network's nodes into meaningful groups. We use a multi-layer quality function to show, on both synthetic and real datasets, that the observation timescale that leads to optimal communities is tightly related to the system's intrinsic aging timescale that can be inferred from the time-stamped network data. The use of temporal information leads to drastically different conclusions on the community structure of real information networks, which challenges the current understanding of the large-scale organization of growing networks. Our findings indicate that before attempting to assess structural patterns of evolving networks, it is vital to uncover the timescales of the dynamical processes that generated them.