多因素驱动下的领域知识网络演化模型:跟风、守旧与创新

当前几种经典的复杂网络模型尚不能有效拟合领域知识网络现实情况,表现为:①以边连线为增长单元难以有效拟合知识网络模块化增长的高聚集效应;②知识增长中除马太效应外,有其他重要因素(如守旧、创新)与之抗衡。因此,有必要根据领域知识自身增长特点探寻一种新的演化模型,以有效实现领域知识的量化分析和预测。本文以典型的领域共词网络为例,从微观的增长视角解析其生成过程、增长方式和多种影响因素,以前人研究结论为证据,提出一种由模块化增长单元组成,并融合跟风、守旧与创新三种影响因素的领域知识网络演化模型;随后,通过实验仿真证明了该模型能更好地拟合现实领域知识网络的整体和微观结构;最后,以此模型为基础,通过进一步的仿真实验揭示了相关因素在领域新知识增长、知识聚集中的影响力度和相互作用。本研究为领域知识增长规律和共现型知识网络结构规律的探索提供了更直接可靠的量化分析基础。

A Network Evolution Model for Domain Knowledge Driven by Multiple Factors: Following Suit,Conservatism, and Innovation

Current complex network models, such as Barabasi-Albert scale-free models and Watts-Strogatz small-world networks, cannot imitate many structural characteristics of the real domain knowledge network. Therefore, it is necessary to explore a new network model suitable for the evolution of domain knowledge. Based on the example of a keyword network, this study discusses the process of domain knowledge generation and its multiple influencing factors. The study then proposes a new network evolution model with keyword modules as basic units, which comprehensively considers following suit, conservatism, and innovation as influencing factors. A simulation experiment showed that the model was effective in fitting the structural characteristics of domain knowledge networks at both macro and micro levels. A further simulation experiment revealed the impacts of following suit and conservatism on growth of new knowledge and knowledge gathering. The proposed model could provide quantitative foundations for future studies on domain knowledge analysis.