Speaker
Prof.
Lada Adamic
(University of Michigan)
Description
Network time series can be used to track and predict the
co-evolution of structure across different networks, and
between a network's structure and its communicated content.
We formulate a measure, temporal conductance, that captures
how unexpected a particular network is given its past
evolution. We find that structure in one network can not
only correlate with the concurrent structure in another
network over the same nodes, but can also help predict how
the second network will evolve. We also find that the
entropy of what is being communicated is captured and can be
predicted by the shape of the communication network.
Smaller, denser networks, with less reciprocity and
clustering correspond to more uniform information content,
while diminished temporal conductance is indicative of
greater change in communicated content.
