  Information diffusion in online social networks is affected by the underlying
network topology, but it also has the power to change it. Online users are
constantly creating new links when exposed to new information sources, and in
turn these links are alternating the way information spreads. However, these
two highly intertwined stochastic processes, information diffusion and network
evolution, have been predominantly studied separately, ignoring their
co-evolutionary dynamics.
  We propose a temporal point process model, COEVOLVE, for such joint dynamics,
allowing the intensity of one process to be modulated by that of the other.
This model allows us to efficiently simulate interleaved diffusion and network
events, and generate traces obeying common diffusion and network patterns
observed in real-world networks. Furthermore, we also develop a convex
optimization framework to learn the parameters of the model from historical
diffusion and network evolution traces. We experimented with both synthetic
data and data gathered from Twitter, and show that our model provides a good
fit to the data as well as more accurate predictions than alternatives.
