A Multiple-Link, Mutually Reinforced Journal-Ranking Model with Applications
Important journals usually guide the research and development directions in academic circles.Therefore,it is necessary to find the important journals among a number of academic journals.This study presents a model named the multiple-link,mutually reinforced journal-ranking (MLMRJR) model based on the PageRank and the Hyperlink-Induced Topics Search (HITS) algorithms that considers not only the quantity and quality of citations in intra-networks,but also the mutual reinforcement in inter-networks.First,the multiple links between four intra-networks and three inter-networks of publication,author,and journal are involved simultaneously.Second,a time factor is added to the publication citation network as the weight of the edges to solve the rank bias problem of the PageRank algorithm.Third,the author citation network and the co-authorship network are considered simultaneously.The results of a case study showed that the proposed MLMRJR model can obtain a reasonable journal ranking.This study provides a systematic view of such field from the perspective of measuring the prestige of journals,which can help researchers decide where to view publications and publish their papers,and help journal editors and organizations evaluate the quality of other journals and focus on the strengths of their own journals.
Journal ranking heterogeneous networks time-aware PageRank HITS
Dejian Yu Wanru Wang Wenyu Zhang
School of Information, Zhejiang University of Finance and Economics, Hangzhou, Zhejiang 310018, China
国内会议
杭州
英文
250-273
2016-11-01（万方平台首次上网日期，不代表论文的发表时间）