Ranking influential nodes in complex networks with structural holes

Ranking influential nodes in complex networks is of great theoretical and practical significance to ensure the safe operations of networks. In view of the important role structural hole nodes usually play in information spreading in complex networks, we propose a novel ranking method of influential nodes using structural holes called E-Burt method, which can be applied to weighted networks. This method fully takes into account the total connectivity strengths of the node in its local scope, the number of the connecting edges and the distributions of the total connectivity strengths on its connecting edges. The simulation results on the susceptible–infectious–recovered (SIR) dynamics suggest that the proposed E-Burt method can rank influential nodes more effectively and accurately in complex networks.

[1]  Chen Zhao,et al.  Analysis of the Chinese Airline Network as multi-layer networks , 2016 .

[2]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Guo Qiang,et al.  Analysis of the spreading influence of the nodes with minimum K-shell value in complex networks , 2013 .

[4]  Deng Hong-zhong.  Evaluation Method for Node Importance based on Node Contraction in Complex Networks , 2006 .

[5]  Wu Yang,et al.  Ranking key nodes in complex networks by considering structural holes , 2015 .

[6]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[7]  Mark E. J. Newman,et al.  Technological Networks and the Spread of Computer Viruses , 2004, Science.

[8]  Fei Xue,et al.  Analysis of structural vulnerabilities in power transmission grids , 2009, Int. J. Crit. Infrastructure Prot..

[9]  Yicheng Zhang,et al.  Identifying influential nodes in complex networks , 2012 .

[10]  Enrico Nardelli,et al.  Finding the most vital node of a shortest path , 2003, Theor. Comput. Sci..

[11]  Hu Xiao.  Evaluation method for node importance in communication networks , 2004 .

[12]  Jian-Guo Liu,et al.  Optimization of Scale-Free Network for Random Failures , 2006 .

[13]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[14]  R. Burt,et al.  Social network analysis: foundations and frontiers on advantage. , 2013, Annual review of psychology.

[15]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

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

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

[18]  Jinlou Zhao,et al.  Multi-attribute integrated measurement of node importance in complex networks. , 2015, Chaos.

[19]  Shengwei Mei,et al.  Identifying node importance in complex networks , 2015 .

[20]  Gang Yan,et al.  Identifying vital edges in Chinese air route network via memetic algorithm , 2016, ArXiv.

[21]  Su Xiaoping,et al.  Leveraging neighborhood “structural holes” to identifying key spreaders in social networks , 2015 .

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

[23]  Konstantin Avrachenkov,et al.  Cooperative Game Theory Approaches for Network Partitioning , 2017, COCOON.