On Two-Dimensional Structural Information of Beijing Transportation Networks Based on Traffic Big Data

Hierarchy is a fundamental characteristic of many complex systems. The methods of structural information have been taken as a prospective way for quantifying dynamical network complexity. This paper is based on the study of the high-dimensional natural structural information entropy in networks. And then we propose a new similarity District Structural Information (DSI) index, which takes the characteristics of network districts into consideration, to analyze the complexity of dynamical network districts. Based on the method, this paper applies the district structural information to explain the equilibrium problem in real-world networks. And taking Beijing traffic network and its districts to complete experiments demonstrates that the DSI index can reflect the equilibrium of the network and the districts effectively.

[1]  Lin Ma,et al.  Information evaluation model of equipment support organization structure based on entropy theory , 2012 .

[2]  Paul Erdös,et al.  On random graphs, I , 1959 .

[3]  Angsheng Li,et al.  Structural Information and Dynamical Complexity of Networks , 2016, IEEE Transactions on Information Theory.

[4]  Ravi Kumar,et al.  Trawling the Web for Emerging Cyber-Communities , 1999, Comput. Networks.

[5]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[6]  Avrim Blum,et al.  A Random-Surfer Web-Graph Model , 2006, ANALCO.

[7]  Claude E. Shannon,et al.  The lattice theory of information , 1953, Trans. IRE Prof. Group Inf. Theory.

[8]  Albert-László Barabási,et al.  Hierarchical organization in complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  A. Vázquez.  Growing network with local rules: preferential attachment, clustering hierarchy, and degree correlations. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.

[11]  A. Rbnyi.  ON THE EVOLUTION OF RANDOM GRAPHS , 2001 .

[12]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[13]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[14]  Eli Upfal,et al.  Stochastic models for the Web graph , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

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