  The problem of finding the optimal set of source nodes in a diffusion network
that maximizes the spread of information, influence, and diseases in a limited
amount of time depends dramatically on the underlying temporal dynamics of the
network. However, this still remains largely unexplored to date. To this end,
given a network and its temporal dynamics, we first describe how continuous
time Markov chains allow us to analytically compute the average total number of
nodes reached by a diffusion process starting in a set of source nodes. We then
show that selecting the set of most influential source nodes in the continuous
time influence maximization problem is NP-hard and develop an efficient
approximation algorithm with provable near-optimal performance. Experiments on
synthetic and real diffusion networks show that our algorithm outperforms other
state of the art algorithms by at least ~20% and is robust across different
network topologies.
