基于复杂网络和语言信息的交互式大规模群体评价方法

针对现有基于语言信息的交互式群体评价方法大多仅适用于中小规模群体的情况，提出一种新的基于复杂网络和语言信息的交互式大规模群体评价方法。首先，将评价者视为网络节点，并依据节点之间的距离构建复杂网络；其次，设计节点紧密度和子群紧密度，并确定节点权重和子群权重；再次，定义群体稳定性指标和群体满意度指标，并以此来判断交互终止和确定阶段权重；最后，在节点密度算子和节点加权算子的基础上，分别对单轮群体意见和多轮评价结果进行集结。文末通过一个实际例子验证了所提方法的有效性与合理性。实例分析表明，该方法能够较为全面、准确地集结交互式大规模群体评价意见，且其结果的满意度亦较为理想。

Interactive Large-scale Group Evaluation Method Based on Complex Network and Linguistic Information

Aiming at the problem that most of interactive group evaluation methods based on linguistic information are only suitable for small-and medium-scale group, a novel interactive large-scale group evaluation method based on linguistic information and complex network is proposed. First, the evaluators are regarded as nodes and a complex network is constructed by calculating the distance between nodes. Second, the node closeness degree and subgroup closeness degree are designed to measure the weights of nodes and subgroups. Then, two indicators of group’s satisfaction degree and group’s stability degree are defined to determine the interactive termination and obtain the stage weights. Finally, node density operator and node weighted operator are given to aggregate static group evaluation information and dynamic evaluation results. One exact example is applied to illustrate the availability and rationality of the proposed method. The research shows that this method can aggregate interactive large-scale group evaluation information more comprehensively and accurately, and the satisfaction degree of the results is more better.