基于Web of Science的PageRank人才挖掘算法

PageRank-based talent mining algorithm based on Web of Science

高水平论文是优秀科技人才的标志性成果之一。聚焦"Web Of Science(WOS)"热点研究学科,在构建学术论文语义Neo4j网络图和挖掘出活跃科研社区基础上,利用PageRank人才挖掘算法实现对科研社区中优秀科研人才的挖掘。首先,对现有的人才挖掘算法进行详细研究和分析;其次,结合WOS论文数据对PageRank人才挖掘算法进行了优化设计和实现,加入了论文发表的时间因子、作者署名排序递减模型、周围作者节点对当前节点的影响、论文被引用量等多维度考量因素。最后,基于热点学科计算机科学某社区近五年的论文数据进行了实验和验证。结果表明,基于社区的挖掘更具有针对性,能够快速定位各学科代表性优秀和潜在人才,且改进后的算法对人才的发现更加客观有效。

The high-level paper is one of the symbolic achievements of excellent scientific talents.Focusing on the"Web of Science(WOS)"hot research disciplines,on the basis of constructing the Neo4j semantic network graph of academic papers and mining active scientific research communities,the PageRank-based talent mining algorithm was used to realize the mining of outstanding scientific research talents in the scientific research communities.Firstly,the existing talent mining algorithms were studied and analyzed in detail.Secondly,combined with the WOS data,the PageRank-based talent mining algorithm was optimized and implemented by adding consideration factors such as the paper publication time factor,the author’s order descending model,the influence of surrounding author nodes on this node,the number of citations of the paper.Finally,experiments and verifications were carried out based on the paper data of the communities of the hot discipline computer science in the past five years.The results show that community-based mining is more targeted,and can quickly find representative excellent and potential talents in various disciplines,and the improved algorithm is more effective and objective.