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知识扩散效率已成为衡量国家或地区科技创新能力的关键指标,但科研协作成本的客观存在制约了知识扩散。社会网络节点间科研协作成本可以通过节点间总距离来描述,主动优化总路径距离有助于提高知识扩散效率,避免节点主体自治的盲目性。本文为此提出一种基于遗传算法的社会网络下知识扩散路径优化模型,并对该模型的算法进行了设计和改进,最后通过仿真案例检验了算法的有效性。
The efficiency of knowledge diffusion has become a key index to measure the capability of science and technology innovation in countries or regions. However, the objective existence of the cost of scientific collaboration restricts the diffusion of knowledge. The cost of research collaboration between nodes in social networks can be described by the total distance between nodes. Taking the initiative to optimize the total path distance helps to improve knowledge diffusion efficiency and avoid the blindness of node autonomy. In this paper, we propose a genetic algorithm-based optimization model of knowledge diffusion path in social network, and design and improve the algorithm of the model. Finally, the validity of the algorithm is verified by simulation examples.