基于个体属性的人类感知网络演化模型

摘要

The authors investigated perceptive networks on individual differences from real group relations. By introducing the Mahalanobis distance in the model, calculated distance between individuals properties to decide priority connection probability, and simulated in different perception dimensions and different perception ability. The results showed that increasing new edges was depended on the property weights associated threshold and perception factor weights associated threshold, which were external environment unified standards, and the interior reason of increasing them was individual nodes attribute differences. The changes of core property factor weights had significant effects on network characteristics.%从个体属性差异角度切入,结合现实群体关系研究感知网络演化模型.模型通过引入马氏定理,模拟在不同感知维度和感知能力下的个体行为,并计算其马氏距离,实现以优先连接概率为前提条件的人类感知网络结构.实验表明;通过优先连接概率阈值和感知因子权重关联度阈值两个外部环境因子,以及个体节点属性差异内部因素,可以共同数据化新旧节点度的动态变化过程,结果显示核心属性因子权重的变化对网络特征影响明显.