ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2015, Vol. 52 ›› Issue (4): 789-797.doi: 10.7544/issn1000-1239.2015.20148407

所属专题: 2015大数据驱动的网络科学

• 网络技术 • 上一篇    下一篇

基于科研合作网络的自动审稿人选择研究

万猛1, 何良华2   

  1. 1(教育部科技发展中心 北京 100080); 2(同济大学计算机科学与技术系 上海 200092) (helianghua@tongji.edu.cn)
  • 出版日期: 2015-04-01
  • 基金资助: 
    基金项目:国家自然科学基金项目(61272267,61170220);教育部新世纪优秀人才支持计划基金项目(NCET-11-0381);中央高校基本科研业务费专项资金项目

Automatic Selection of Paper Reviewer Based on Scientific Collaboration Network

Wan Meng1, He Lianghua2   

  1. 1(Center for Science and Technology Development, Ministry of Education, Beijing 100080); 2(Department of Computer Science and Technology, Tongji University, Shanghai 200092)
  • Online: 2015-04-01

摘要: 主要研究科研合作网络(scientific collaboration network, SCN)中选择审稿人的2个核心问题:网络构建和社区聚类.基于杂志论文的审稿人主要来自本杂志作者及审稿人应尽量评审与自己无关的科研工作这2个事实,通过论文中作者的排名计算所有作者之间的合作关系,构建归一化科研合作网络.考虑到网络中边的稀疏性,设计了合作压缩感知算法来计算不同作者间的社区类型,进行科研合作社区聚类.在模拟数据及2个真实期刊作者库上开展了多个实验.由于没有一个客观标准去评估所选出审稿人的合适性,通过网络中顶点连接矩阵的自动聚类性评估所构建科研网络的性能,通过作者合作团体的检测准确性来评价审稿人挑选的有效性.从实验结果可以看出,提出的网络构建方法具有较好的顶点聚类性;和经典算法相比,合作压缩感知社区检测算法在检测速度和稳定性方面具有很大的优势,审稿人挑选正确率提高了大约60%.

关键词: 科研合作网络, 稀疏表示, 正交匹配追踪, 论文评审, 审稿人选择

Abstract: In this paper, we study two tightly coupled topics in selecting paper reviewers from authors’ scientific collaboration network (SCN): network construction and community detection. Based on the fact that the authors of one journal can be selected as reviewers and the reviewers of one manuscript should come from different research communities, we firstly evaluate the collaboration among all authors according to their signatures and construct the normalized collaboration network. For the second key problem of detecting the communities of one scientific collaboration network, considering it is much sparse and has few connections with inter community for one vertex, we apply the method of orthogonal matching pursuit to calculate compressive collaboration information. We conduct several experiments on simulated and real journal author datasets. Although there is no standard to evaluate different kinds of scientific collaboration network, the community detection accuracy rate and the stability of all authors are used to evaluate the performance of the proposed method. We can see from the vertex linkage matrix that our designed scientific collaboration network has good character of vertex grouping. The extensive study of our detection method in simulated data shows that the proposed method has a great advantage in the detection rate and stability. The significant improvement is about 60% compared with the classic methods.

Key words: scientific collaboration network (SCN), sparse representation, orthogonal matching pursuit, paper review, reviewer selection

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