论文标题
基于负载中心性的研究学者兴趣挖掘方法
Research Scholar Interest Mining Method based on Load Centrality
论文作者
论文摘要
在大数据时代，可以通过论文，专利和其他数据对研究人员的研究结果进行合作研究，以研究研究人员的作用，并在分析结果分析中产生结果。对于现实研究和应用中发现的重要问题，本文还提出了基于负载中心（LCBIM）的研究学者兴趣挖掘算法，该算法可以根据研究人员的研究论文和专利数据来准确地解决该问题。在研究的各个领域中创意算法的图形汇总想法，通过汇总社区来生成的主题图，使用生成的主题信息在具有相似或相似的主题空间中构造，并利用关键字来构建一个或多个主题。每个主题的区域结构可用于密切计算节点的中心性研究模型的重量，该模型可以在完整的覆盖原理中分析该场地。基于本文提出的负载率中心的科学研究合作可以有效地从论文和语料库中提取科学研究学者的利益。
In the era of big data, it is possible to carry out cooperative research on the research results of researchers through papers, patents and other data, so as to study the role of researchers, and produce results in the analysis of results. For the important problems found in the research and application of reality, this paper also proposes a research scholar interest mining algorithm based on load centrality (LCBIM), which can accurately solve the problem according to the researcher's research papers and patent data. Graphs of creative algorithms in various fields of the study aggregated ideas, generated topic graphs by aggregating neighborhoods, used the generated topic information to construct with similar or similar topic spaces, and utilize keywords to construct one or more topics. The regional structure of each topic can be used to closely calculate the weight of the centrality research model of the node, which can analyze the field in the complete coverage principle. The scientific research cooperation based on the load rate center proposed in this paper can effectively extract the interests of scientific research scholars from papers and corpus.
