A New Method for Evaluating Node Importance in Complex Networks Based on Data Field Theory

Estimating of the node importance in complex networks will help us research the core issues of real networks. Evaluating node importance with a single metric is incomplete and limited. This paper proposed a new measure of evaluating node importance. Its basic idea is sequencing the topology potential of node which is based on data field theory and combined with node-degree distribution, and identifying important nodes according to the topological potential. Simulation results of a real network show the feasibility and rationality of the new method.

[1]  L. Freeman,et al.  Centrality in social networks: ii. experimental results☆ , 1979 .

[2]  Jianlin Li,et al.  Agriculture Emergency Decision System Based on Semantic Web Services , 2007, 2007 International Conference on Convergence Information Technology (ICCIT 2007).

[3]  H. W. Corley,et al.  Most vital links and nodes in weighted networks , 1982, Oper. Res. Lett..

[4]  He Nan,et al.  Evaluate Nodes Importance in the Network Using Data Field Theory , 2007, 2007 International Conference on Convergence Information Technology (ICCIT 2007).

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

[6]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

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

[8]  Deyi Li,et al.  Artificial Intelligence with Uncertainty , 2004, CIT.

[9]  Vladimir Batagelj,et al.  Centrality in Social Networks , 1993 .

[10]  John Scott.  Social Network Analysis , 1988 .