【摘要】 学术影响力评价一直是文献计量学领域的一个研究热点。已有的大多基于数据挖掘的学术影响力评价方法忽略了恶意活动产生的影响,导致评价结果欠佳。为解决这一问题,提出了一种名为ReputeRank的新方法,该方法采用信誉机制来评估引文网络中出版物的有效性。信誉机制包括3个阶段:种子集选择阶段、信誉传播阶段和集成计算阶段。首先,ReputeRank利用SCI期刊分区信息选择引文网络中潜在的好种子和坏种子;然后,根据信誉传播思想,信誉度良好的种子指向的论文通常具有更高的可信度,而信誉度不好的种子指向的论文通常具有较低的可信度,该方法使用TrustRank和Anti-TrustRank评价公式在引文网络中迭代传播信任值和不信任值;最后,根据引文网络中的信任值和不信任值,利用综合集成公式对每篇论文计算评分,并根据评分结果对所有论文降序排列。在KDD cup2003数据集上的实验结果表明,与3种影响力评价方法 PageRank,CountDegree和SPRank相比,ReputeRank能够获得更优的效果。更多还原

【Abstract】 Evaluating the scientific value of publications has always been a research focus in the field of bibliometrics.However,some mainstream methods based on data mining overlook the influence of malicious activities and result in poor evaluation results.To solve this problem,this paper proposed a new method named ReputeRank,which employs a creditworthiness mechanism to evaluate the effectiveness of publications in the citation network.The creditworthiness mechanism consists of the seeds selection phase,the spread credit phase and the integrated computation phase.First,ReputeRank employs background information on the division of SCI Periodicals to select potential good seeds and bad seeds in the citation network.Then,in light of assumption that good credibility seeds pointing to papers which usually have a higher credible degree while bad credibility seeds pointing to papers which often have a lower credible degree,the method uses TrustRank and Anti-TrustRank evaluation formula to iteratively spread trust values and distrust values over the citation network.Finally,according to the trust and distrust values in the citation network,the method utilizes an integrated equation to comprehensively compute the score value of each paper and arranges all papers in the descending order of the score values.The experimental results on KDD cup 2003 datasets demonstrate that ReputeRank has good performance of effectiveness and robustness compared with PageRank,CountDegree and SPRank.更多还原