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Controlling a complex network towards a desire state is of great importance in many applications. Existing works present an approximate algorithm to find the driver nodes used to control partial nodes of the network. However, the driver nodes obtained by this algorithm depend on the matching order of nodes and cannot get the optimum results. Here we present a novel algorithm to find the driver nodes for target control based on preferential matching. The algorithm elaborately arrange the matching order of nodes in order to minimize the size of the driver nodes set. The results on both synthetic and real networks indicate that the performance of proposed algorithm are better than the previous one. The algorithm may have various application in controlling complex networks.
Minimum driver node sets (MDSs) play an important role in studying the structural controllability of complex networks. Recent research has shown that MDSs tend to avoid high-degree nodes. However, this observation is based on the analysis of a small
A self-organization of efficient and robust networks is important for a future design of communication or transportation systems, however both characteristics are incompatible in many real networks. Recently, it has been found that the robustness of
A preferential attachment model for a growing network incorporating deletion of edges is studied and the expected asymptotic degree distribution is analyzed. At each time step $t=1,2,ldots$, with probability $pi_1>0$ a new vertex with one edge attach
Innovation is the driving force of human progress. Recent urn models reproduce well the dynamics through which the discovery of a novelty may trigger further ones, in an expanding space of opportunities, but neglect the effects of social interactions
Community detection or clustering is a crucial task for understanding the structure of complex systems. In some networks, nodes are permitted to be linked by either positive or negative edges; such networks are called signed networks. Discovering com