通过相互加强,实现科学文献的有效和公正的排名

Xiaorui Jiang, Xiaoping Sun, H. Zhuge
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引用次数: 39

摘要

帮助研究人员从包含作者、论文和地点信息的大型文献集中找到有价值的科学论文是很重要的。基于图的算法已经被提出,根据由引用和合著者关系形成的网络对论文进行排名。本文提出了一种新的基于图的排名框架MutualRank,该框架整合了论文、研究人员和场地网络之间的相互强化关系,从而获得比以往基于图的排名方法更综合、更准确、更公平的排名结果。MutualRank利用文献收集数据集中可获得的论文、作者和地点之间的网络结构信息,建立了一个包括网络内和网络间信息的统一的相互强化模型,同时对论文、作者和地点进行排名。为了评估,我们从15所顶尖大学的研究生水平计算语言学课程网站上收集了一组推荐论文作为基准,并采用不同的方法来估计论文的重要性。结果表明,MutualRank在论文排名和研究人员排名方面都大大优于竞争对手,包括page - erank、HITS和CoRank。实验结果也证明了MutualRank对场馆的排序是合理的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards an effective and unbiased ranking of scientific literature through mutual reinforcement
It is important to help researchers find valuable scientific papers from a large literature collection containing information of authors, papers and venues. Graph-based algorithms have been proposed to rank papers based on networks formed by citation and co-author relationships. This paper proposes a new graph-based ranking framework MutualRank that integrates mutual reinforcement relationships among networks of papers, researchers and venues to achieve a more synthetic, accurate and fair ranking result than previous graph-based methods. MutualRank leverages the network structure information among papers, authors, and their venues available from a literature collection dataset and sets up a unified mutual reinforcement model that involves both intra- and inter-network information for ranking papers, authors and venues simultaneously. To evaluate, we collect a set of recommended papers from websites of graduate-level computational linguistics courses of 15 top universities as the benchmark and apply different methods to estimate paper importance. The results show that MutualRank greatly outperforms the competitors including Pag-eRank, HITS and CoRank in ranking papers as well as researchers. The experimental results also demonstrate that venues ranked by MutualRank are reasonable.
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