大数据中网络节点拓扑不稳定性的评估模型设计

大数据中网络节点拓扑结构复杂且具有明显的不稳定性,当前网络节点拓扑不稳定性评估模型大多依据社会网络分析方法理论,从不同角度表示具有不同拓扑结构特性的网络元件,获取的评估结果不充分、不可靠。为此,提出一种新的大数据中网络节点拓扑不稳定性评估模型,通过节点收缩法对大数据中网络节点重要性进行评估。针对大数据中网络节点拓扑不稳定性的评估,结合风险函数丰富熵的内涵,将事件的风险函数在效用系数空间中的平均值看作网络的效用风险熵,依据得到的效用风险熵对效用风险熵权重进行计算。将节点收缩法和效用风险熵权重结合在一起,重新考虑权重的影响,给出新的大数据中网络凝聚度,得到大数据中网络节点拓扑不稳定性评估模型。实验结果表明,所提方法评估可靠性很高。

Big data node in the network topology is not stability evaluation model of the design

big data nodes in the network topology structure is complex and has obvious instability, most of the current node topology network instability assessment model based on the theory of social network analysis method, from different angles said network components with different topology characteristics, access to the evaluation results of inadequate and unreliable.For this, put forward a new big data node in the network topology instability assessment model, through the node contraction method to evaluate large data in network node importance.For large data in network nodes topology instability assessment, combined with the connotation of risk function rich entropy, the event risk function in the utility coefficient of average in space as the utility of network risk entropy, on the basis of the utility risk entropy of utility risk entropy weight to calculate.The node shrinkage method and utility risk entropy weight together, to consider the effect of weight, given new big data network agglomeration degree, get big data node in the network topology stability evaluation model.The experimental results show that the proposed method is of high reliability assessment.