SELF-SIMILARITY IN COMPLEX NETWORKS: FROM THE VIEW OF THE HUB REPULSION

Complex networks are widely used to model the structure of many complex systems in nature and society. Recently, fractal and self-similarity of complex networks have attracted much attention. It is observed that hub repulsion is the key principle that leads to the fractal structure of networks. Based on the principle of hub repulsion, the metric in complex networks is redefined and a new method to calculate the fractal dimension of complex networks is proposed in this paper. Some real complex networks are investigated and the results are illustrated to show the self-similarity of complex networks.

[1]  Jianwei Wang,et al.  ORIGIN OF THE STRONGER ROBUSTNESS AGAINST CASCADING FAILURES OF COMPLEX NETWORKS: A MITIGATION STRATEGY PERSPECTIVE , 2013 .

[2]  Hans J. Herrmann,et al.  Optimal box-covering algorithm for fractal dimension of complex networks , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Hyun-Joo Kim,et al.  ANALYSIS OF A COMPLEX NETWORK OF PHYSICS CONCEPTS , 2012 .

[4]  O. Shanker.  ALGORITHMS FOR FRACTAL DIMENSION CALCULATION , 2008 .

[5]  Ji Qi,et al.  Efficiency Dynamics on Scale-Free Networks with Communities , 2010 .

[6]  S. Havlin,et al.  How to calculate the fractal dimension of a complex network: the box covering algorithm , 2007, cond-mat/0701216.

[7]  S. Havlin,et al.  Self-similarity of complex networks , 2005, Nature.

[8]  B. Gee,et al.  Biologic Complexity in Sickle Cell Disease: Implications for Developing Targeted Therapeutics , 2013, TheScientificWorldJournal.

[9]  Damian Roqueiro,et al.  A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene Modules , 2013, TheScientificWorldJournal.

[10]  Jing Wang,et al.  SIMILARITY INDEX BASED ON THE INFORMATION OF NEIGHBOR NODES FOR LINK PREDICTION OF COMPLEX NETWORK , 2013 .

[11]  D. Lusseau,et al.  The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations , 2003, Behavioral Ecology and Sociobiology.

[12]  O. Shanker,et al.  DEFINING DIMENSION OF A COMPLEX NETWORK , 2007 .

[13]  H. Jose Antonio Martin,et al.  Solving Hard Computational Problems Efficiently: Asymptotic Parametric Complexity 3-Coloring Algorithm , 2011, PloS one.

[14]  O. Shanker,et al.  Graph zeta function and dimension of complex network , 2007 .

[15]  Tiesong Hu,et al.  DETECTING CHAOS TIME SERIES VIA COMPLEX NETWORK FEATURE , 2011 .

[16]  Zhong-Yuan Jiang,et al.  IMPROVING NETWORK TRANSPORT EFFICIENCY BY EDGE REWIRING , 2013 .

[17]  Xiaoge Zhang,et al.  A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks , 2013, PloS one.

[18]  Peter Langfelder,et al.  When Is Hub Gene Selection Better than Standard Meta-Analysis? , 2013, PloS one.

[19]  S. Mahadevan,et al.  Identifying influential nodes in weighted networks based on evidence theory , 2013 .

[20]  S. Mahadevan,et al.  A modified evidential methodology of identifying influential nodes in weighted networks , 2013 .

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

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

[23]  Shlomo Havlin,et al.  Origins of fractality in the growth of complex networks , 2005, cond-mat/0507216.

[24]  Almerima Jamakovic,et al.  On the relationships between topological measures in real-world networks , 2008, Networks Heterog. Media.

[25]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[26]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.