  Extracting a proper dynamic network for modelling a time-dependent complex
system is an important issue. Building a correct model is related to finding
out critical time points where a system exhibits considerable change. In this
work, we propose to measure network similarity to detect proper time intervals.
We develop three similarity metrics, node, link, and neighborhood similarities,
for any consecutive snapshots of a dynamic network. Rather than a label or a
user-defined threshold, we use statistically expected values of proposed
similarities under a null-model to state whether the system changes critically.
We experimented on two different data sets with different temporal dynamics:
The Wi-Fi access points logs of a university campus and Enron emails. Results
show that, first, proposed similarities reflect similar signal trends with
network topological properties with less noisy signals, and their scores are
scale invariant. Second, proposed similarities generate better signals than
adjacency correlation with optimal noise and diversity. Third, using
statistically expected values allows us to find different time intervals for a
system, leading to the extraction of non-redundant snapshots for dynamic
network modelling.
