  Understanding how people interact and socialize is important in many contexts
from disease control to urban planning. Datasets that capture this specific
aspect of human life have increased in size and availability over the last few
years. We have yet to understand, however, to what extent such electronic
datasets may serve as a valid proxy for real life social interactions. For an
observational dataset, gathered using mobile phones, we analyze the problem of
identifying transient and non-important links, as well as how to highlight
important social interactions. Applying the Bluetooth signal strength parameter
to distinguish between observations, we demonstrate that weak links, compared
to strong links, have a lower probability of being observed at later times,
while such links--on average--also have lower link-weights and probability of
sharing an online friendship. Further, the role of link-strength is
investigated in relation to social network properties.
