改进的随机游走模型节点排序方法

复杂网络节点重要性排序是研究复杂网络特性的重要方面之一，被广泛应用于数据挖掘、Web搜索、社会网络分析等众多研究领域。基于物理学场论模型，提出改进的随机游走模式的节点重要性排序算法，即通过节点之间相互作用的场力来确定随机游走模型中的Markov转移矩阵，这样可以对节点重要性排序作出更加准确真实的评估。实验结果表明，所采用的节点重要性评估方法能更合理地解释节点重要性的意义，并且可以给出更加真实精确的节点重要性的评估结果。

Modified nodes ranking method using random walk model

Nodes importance ranking in complex network is one of the most important aspects of studying the properties of complex network,which has been widely used in data mining,web searching,analysis of social network and so on.The paper puts forward a new modified random walk model nodes ranking algorithm based on field theory from the physics subject to compute the Markov transfer matrix of random walk model by the field force between the interacting nodes, which can give more exact and actual evaluation for nodes importance.Preliminary experimental results show that the nodes importance measurement is more reasonable to interpret the nodes importance and can compute the results of nodes importance more precisely.