  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.
