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A Complex Network Model for Analyzing Railway Accidents Based on the maximal information coefficient

Communications in Theoretical Physics 2016年第10期65卷 459-466页

作者： Fu-Bo Shao Ke-Ping Li State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University

It is an important issue to identify important influencing factors in railway accident *** this paper,employing the good measure of dependence for two-variable relationships,the maximal information coefficient(MIC),which can capture a wide range of associations,a complex network model for railway accident analysis is designed in which nodes denote factors of railway accidents and edges are generated between two factors of which MIC values are larger than or equal to the dependent *** variety of network structure is *** the increasing of the dependent criterion,the network becomes to an approximate scale-free ***,employing the proposed network,important influencing factors are *** we find that the annual track density-gross tonnage factor is an important factor which is a cut vertex when the dependent criterion is equal to *** the network,it is found that the railway development is unbalanced for different states which is consistent with the fact.

关键词： railway accidents complex network the maximal information coefficient

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The Theoretical and Experimental Analysis of the maximal information coefficient Approximate Algorithm

引用

Journal of Systems Science and information 2021年第1期9卷 95-104页

作者： Fubo SHAO Hui LIU State Key Laboratory of Rail Trufic Control and Safety Beijing Jiaotong UniversityBeijing 100044China The Post-Doctor Stalion of the CRRC Corporation Limited Beijing 100067ChinaCRRC AcademyBeijing 100067China School of Mathematics and Physics Qingdao Universily of Science and TechnologyQingdao 266061China

In the era of big data,correlation analysis is significant because it can quickly detect the correlation between *** then,it has been received much *** to the good properties of generality and equitability of the maximal information coefficient(MIC),MIC is a hotspot in the research of correlation ***,if the original approximate algorithm of MIC is directly applied into mining correlations in big data,the computation time is very *** the theoretical time complexity of the original approximate algorithm is analyzed in depth and the time complexity is n2.4 when parameters are *** the experiments show that the large number of candidate partitions of random relationships results in long computation *** analysis is a good preparation for the next step work of designing new fast algorithms.

关键词： statistical correlation the maximal information coefficient approximate algorithm mutual information dynamic programming algorithm

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