  Random walk is one of the basic mechanisms found in many network
applications. We study the epidemic spreading dynamics driven by biased random
walks on complex networks. In our epidemic model, each time infected nodes
constantly spread some infected packets by biased random walks to their
neighbor nodes causing the infection of the susceptible nodes that receive the
packets. An infected node get recovered from infection with a fixed
probability. Simulation and analytical results on model and real-world networks
show that the epidemic spreading becomes intense and wide with the increase of
delivery capacity of infected nodes, average node degree, homogeneity of node
degree distribution. Furthermore, there are corresponding optimal parameters
such that the infected nodes have instantaneously the largest population, and
the epidemic spreading process covers the largest part of a network.
