  Link prediction -- to identify potential missing or spurious links in
temporal network data -- has typically been based on local structures, ignoring
long-term temporal effects. In this chapter, we propose link-prediction methods
based on agents' behavioral synchrony. Since synchronous behavior signals
similarity and similar agents are known to have a tendency to connect in the
future, behavioral synchrony could function as a precursor of contacts and,
thus, as a basis for link prediction. We use four data sets of different sizes
to test the algorithm's accuracy. We compare the results with traditional link
prediction models involving both static and temporal networks. Among our
findings, we note that the proposed algorithm is superior to conventional
methods, with the average accuracy improved by approximately 2% - 5%. We
identify different evolution patterns of four network topologies -- a proximity
network, a communication network, transportation data, and a collaboration
network. We found that: (1) timescale similarity contributes more to the
evolution of the human contact network and the human communication network; (2)
such contribution is not observed through a transportation network whose
evolution pattern is more dependent on network structure than on the behavior
of regional agents; (3) both timescale similarity and local structural
similarity contribute to the collaboration network.
