  Selecting the optimal subset from all vertices as seeds to maximize the
influence in a social network has been a task of interest. Various methods have
been proposed to select the optimal vertices in a static network, however, they
are challenged by the dynamics, i.e. the time-dependent variation of the social
network structure. Such dynamics hinder the paradigm for static networks and
leaves a seemingly unbridgeable gap between algorithms of influence
maximization on static networks and those on dynamic ones. In this paper, we
extend our previous work and demonstrate that conjugate learning automata (an
elementary variant of reinforcement learning) that have been successfully
applied to maximize influence on static networks can be applied to dynamic
networks as well. The network dynamics is measured by the variation of the
influence range and absorbed into the learning procedure. Our proposal
delicately formulates the effect of network dynamics: the more the influence
range varies, the more likely the seeds are to be learned from scratch. Under
this assumption, the continuity of the network variation is fully taken
advantage of. Experimental results on both synthetic and real-world networks
verify the privileges of our proposal against alternative methods.
