摘要：Many efforts in studying network structure and dynamics have been engaged, among which the research on the node search or navigation is one of the most important branches. With a brief analysis of the existing search strategies, the MDS (maximum degree strategy) is found not applicable to large-scale with even degree distribution networks. By importing the minimum cluster coefficient as one parameter for the node search, the MCMDS (Minimum Cluster Coefficient and Maximum Degree Strategy) is presented in order to better its search performance for networks of high power-law exponents. Specific implementation steps for the MCMDS are provided. The strategy is simulated and the results are analyzed. In the end a test to verify the efficiency of the MCMDS on the networks with real data is employed. Simulation results show that the MCMDS presented in the paper can improve the performance in both search steps and search time for its even degree distribution.
