  Inferring the network topology from the dynamics is a fundamental problem
with wide applications in geology, biology and even counter-terrorism. Based on
the propagation process, we present a simple method to uncover the network
topology. The numerical simulation on artificial networks shows that our method
enjoys a high accuracy in inferring the network topology. We find the infection
rate in the propagation process significantly influences the accuracy, and each
network is corresponding to an optimal infection rate. Moreover, the method
generally works better in large networks. These finding are confirmed in both
real social and nonsocial networks. Finally, the method is extended to directed
networks and a similarity measure specific for directed networks is designed.
