  With the emerging of new networks, such as wireless sensor networks, vehicle
networks, P2P networks, cloud computing, mobile Internet, or social networks,
the network dynamics and complexity expands from system design, hardware,
software, protocols, structures, integration, evolution, application, even to
business goals. Thus the dynamics and uncertainty are unavoidable
characteristics, which come from the regular network evolution and unexpected
hardware defects, unavoidable software errors, incomplete management
information and dependency relationship between the entities among the emerging
complex networks. Due to the complexity of emerging networks, it is not always
possible to build precise models in modeling and optimization (local and
global) for networks. This paper presents a survey on probabilistic modeling
for evolving networks and identifies the new challenges which emerge on the
probabilistic models and optimization strategies in the potential application
areas of network performance, network management and network security for
evolving networks.
