While computer modeling and simulation are crucial for understanding scientometrics, their practical use in literature remains somewhat limited. In this study, we establish a joint coauthorship and citation network using preferential attachment. As papers get published, we update the coauthorship network based on each paper's author list, representing the collaborative team behind it. This team is formed considering the number of collaborations each author has, and we introduce new authors at a fixed probability, expanding the coauthorship network. Simultaneously, as each paper cites a specific number of references, we add an equivalent number of citations to the citation network upon publication. The likelihood of a paper being cited depends on its existing citations, fitness value, and age. Then we calculate the journal impact factor and h-index, using them as examples of scientific impact indicators. After thorough validation, we conduct case studies to analyze the impact of different parameters on the journal impact factor and h-index. The findings reveal that increasing the reference number N or reducing the paper's lifetime {\theta} significantly boosts the journal impact factor and average h-index. On the other hand, enlarging the team size m without introducing new authors or decreasing the probability of newcomers p notably increases the average h-index. In conclusion, it is evident that various parameters influence scientific impact indicators, and their interpretation can be manipulated by authors. Thus, exploring the impact of these parameters and continually refining scientific impact indicators are essential. The modeling and simulation method serves as a powerful tool in this ongoing process, and the model can be easily extended to include other scientific impact indicators and scenarios.

翻译：尽管计算机建模与仿真对于理解科学计量学至关重要，但其在文献中的实际应用仍相对有限。本研究基于优先连接机制构建了合著网络与引文网络的联合模型。论文发表时，我们根据每篇论文的作者列表更新合著网络，该列表代表了其背后的合作团队。团队的形成考虑了每位作者的合作次数，并以固定概率引入新作者以扩展合著网络。同时，每篇论文引用特定数量的参考文献时，我们会在其发表时向引文网络添加等量的引用次数。论文被引用的可能性取决于其现有引用量、适应度值与时效性。随后，我们以期刊影响因子和h指数为例计算科学影响力指标。经过全面验证后，通过案例研究分析不同参数对期刊影响因子和h指数的影响。结果表明，增加参考文献数量N或缩短论文生命周期θ会显著提升期刊影响因子和平均h指数。另一方面，在不引入新作者的情况下扩大团队规模m，或降低新作者加入概率p，会显著提高平均h指数。结论表明，不同参数对科学影响力指标具有显著影响，且其解读可能受作者操纵。因此，探究这些参数的影响并持续完善科学影响力指标至关重要。建模与仿真方法在此持续进程中扮演着重要角色，该模型可轻松扩展至其他科学影响力指标及场景。