基于研究者发文序列的研究领域扩散

当前，随着数字图书馆技术的发展，大量文献数据为研究者进行科学研究提供了便利，但这些海量信息也给用户总结和理解文献数据带来了困难，为此，迫切需要智能化的方法来辅助研究者理解这些相关数据，特别是快速和准确地理解其所对应领域的发展和演化。针对这一问题，通过能够反映主题趋势的重要内容（如顶级的国际会议）对主题进行分析，利用基于传递闭包的动态贝叶斯网络构造模型，分析研究者在参加一个学术会议后的活动来研究领域的扩散，从而理解研究领域的变化。

Study on research field diffusion based on author publication track record

With the development of digital library technology, many digital literatures have come into being. It is very important to mine these sources for providing convenience for the user, and it is paramount to analyze these texts to help user understand topic evolution and development. Dynamic Bayesian network model was used to investigate the research field diffusion, and experiments on real data sets indicate that the proposed method can help researchers get a better insight into research field diffusion for various research fields.