An Improved Search Strategy for Even Degree Distribution Networks

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 ( M inimum C luster C oefficient and M aximum D egree S trategy) is presented in order to better its search performance for networks of high power-law exponents. Specific implementation steps for the MCMDS are provided. T he 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.

[1]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[2]  Albert-László Barabási,et al.  Internet: Diameter of the World-Wide Web , 1999, Nature.

[3]  Mark Newman,et al.  The structure and function of networks , 2002 .

[4]  Guanrong Chen,et al.  Complex networks: small-world, scale-free and beyond , 2003 .

[5]  S. Havlin,et al.  Dimension of spatially embedded networks , 2011 .

[6]  Romualdo Pastor-Satorras,et al.  Random walks on temporal networks. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  S. Strogatz Exploring complex networks , 2001, Nature.

[8]  K. Goh,et al.  Universal behavior of load distribution in scale-free networks. , 2001, Physical review letters.

[9]  S. Redner,et al.  Connectivity of growing random networks. , 2000, Physical review letters.

[10]  Jon M. Kleinberg,et al.  The small-world phenomenon: an algorithmic perspective , 2000, STOC '00.

[11]  Béla Bollobás,et al.  The diameter of random regular graphs , 1982, Comb..

[12]  Wenjun Xiao,et al.  CayleyCCC: A Robust P2P Overlay Network with Simple Routing and Small-World Features , 2011, J. Networks.

[13]  M E J Newman,et al.  Identity and Search in Social Networks , 2002, Science.

[14]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[15]  Adilson E. Motter,et al.  Networks in Motion , 2012, ArXiv.

[16]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[17]  Wei Xiong,et al.  A Novel Network Traffic Anomaly Detection Model Based on Superstatistics Theory , 2011, J. Networks.

[18]  Ingo Scholtes,et al.  Betweenness Preference: Quantifying Correlations in the Topological Dynamics of Temporal Networks , 2012, Physical review letters.

[19]  K. Choromanski,et al.  Scale-Free Graph with Preferential Attachment and Evolving Internal Vertex Structure , 2013 .