Multi-index algorithm of identifying important nodes in complex networks based on linear discriminant analysis

The evaluation of node importance has great significance to complex network, so it is important to seek and protect important nodes to ensure the security and stability of the entire network. At present, most evaluation algorithms of node importance adopt the single-index methods, which are incomplete and limited, and cannot fully reflect the complex situation of network. In this paper, after synthesizing multi-index factors of node importance, including eigenvector centrality, betweenness centrality, closeness centrality, degree centrality, mutual-information, etc., the authors are proposing a new multi-index evaluation algorithm of identifying important nodes in complex networks based on linear discriminant analysis (LDA). In order to verify the validity of this algorithm, a series of simulation experiments have been done. Through comprehensive analysis, the simulation results show that the new algorithm is more rational, effective, integral and accurate.

[1]  Jia Yan,et al.  Integrating attributes of nodes solves the community structure partition effectively , 2014 .

[2]  José Rui Figueira,et al.  Modeling centrality measures in social network analysis using bi-criteria network flow optimization problems , 2013, Eur. J. Oper. Res..

[3]  Marie-Claude Boily,et al.  Dynamical systems to define centrality in social networks , 2000, Soc. Networks.

[4]  Wu Jun,et al.  Finding the most vital node by node contraction in communication networks , 2005, Proceedings. 2005 International Conference on Communications, Circuits and Systems, 2005..

[5]  Tommy W. S. Chow,et al.  Soft label based Linear Discriminant Analysis for image recognition and retrieval , 2014, Comput. Vis. Image Underst..

[6]  Damon Horowitz,et al.  The anatomy of a large-scale social search engine , 2010, WWW '10.

[7]  Yi Zhang,et al.  A new clustering algorithm based on data field in complex networks , 2013, The Journal of Supercomputing.

[8]  Mark E. J. Newman.  A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[9]  U. Rajendra Acharya,et al.  Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform , 2013, Knowl. Based Syst..

[10]  J. Jin,et al.  Multi-index evaluation algorithm based on principal component analysis for node importance in complex networks , 2012, IET Networks.

[11]  Ping Hu,et al.  A HIGH ROBUSTNESS AND LOW COST CASCADING FAILURE MODEL BASED ON NODE IMPORTANCE IN COMPLEX NETWORKS , 2014 .

[12]  U. Rajendra Acharya,et al.  ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform , 2013, Biomed. Signal Process. Control..

[13]  H. Zha,et al.  Principal manifolds and nonlinear dimensionality reduction via tangent space alignment , 2004, SIAM J. Sci. Comput..

[14]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[15]  Sung-Kwun Oh,et al.  Design of face recognition algorithm using PCA -LDA combined for hybrid data pre-processing and polynomial-based RBF neural networks : Design and its application , 2013, Expert Syst. Appl..

[16]  Qiang Guo,et al.  Ranking the spreading influence in complex networks , 2013, ArXiv.

[17]  Lv Le,et al.  A New Method for Evaluating Node Importance in Complex Networks Based on Data Field Theory , 2010, 2010 First International Conference on Networking and Distributed Computing.

[18]  Phillip Bonacich,et al.  Some unique properties of eigenvector centrality , 2007, Soc. Networks.

[19]  Xiangyun Hu,et al.  An analytical model for spontaneous imbibition in fractal porous media including gravity , 2012 .

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

[21]  Vito Latora,et al.  Controlling centrality in complex networks , 2011, Scientific Reports.

[22]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[23]  Zhang Minqing,et al.  Evaluating node importance in complex networks based on factor analysis , 2011, Proceedings of 2011 International Conference on Computer Science and Network Technology.

[24]  S. Mahadevan,et al.  A new method of identifying influential nodes in complex networks based on TOPSIS , 2014 .

[25]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[26]  D S Callaway,et al.  Network robustness and fragility: percolation on random graphs. , 2000, Physical review letters.

[27]  Kun Zhang,et al.  Evaluating the importance of nodes in complex networks based on principal component analysis and grey relational analysis , 2011, 2011 17th IEEE International Conference on Networks.

[28]  John Skvoretz,et al.  Node centrality in weighted networks: Generalizing degree and shortest paths , 2010, Soc. Networks.

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

[30]  Eric Brill,et al.  Improving web search ranking by incorporating user behavior information , 2006, SIGIR.

[31]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[32]  W. Torgerson.  Multidimensional scaling: I. Theory and method , 1952 .

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

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