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The paper attempts to provide an alternative method for measuring the importance of scientific papers based on the Google’s PageRank. The method is a meaningful extension of the common integer counting of citations and is then experimented for bringing PageRank to the citation analysis in a large citation network. It offers a more integrated picture of the publications’ influence in a specific field. We firstly calculate the PageRanks of scientific papers. The distributional characteristics and comparison with the traditionally used number of citations are then analyzed in detail. Furthermore, the PageRank is implemented in the evaluation of research influence for several countries in the field of Biochemistry and Molecular Biology during the time period of 2000–2005. Finally, some advantages of bringing PageRank to the citation analysis are concluded. 相似文献
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专利作为企业间技术竞争情报的重要信息源,从中可以发现竞争对手的技术发展战略及趋势。利用专利引用关系构建了企业引用网络,借鉴链接分析中PageRank算法的思想,提出了企业技术影响力的评价方法,并以USTPO美国专利数据库1975至1999年液压制动领域的专利为数据样本进行了实证分析。分析结果表明,与基于被引次数的传统评价指标相比,新评价方法综合考虑了引用企业数量、企业自身影响力、企业间引用强度三方面的因素,因此可以更加客观地评估企业的技术影响能力,进而帮助企业准确识别行业内的技术竞争对手。 相似文献
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《Information processing & management》2023,60(1):103156
Hiring appropriate editors, chairs and committee members for academic journals and conferences is challenging. It requires a targeted search for high profile scholars who are active in the field as well as in the publication venue. Many author-level metrics have been employed for this task, such as the h-index, PageRank and their variants. However, these metrics are global measures which evaluate authors’ productivity and impact without differentiating the publication venues. From the perspective of a venue, it is also important to have a localised metric which can specifically indicate the significance of academic authors for the particular venue. In this paper, we propose a relevance-based author ranking algorithm to measure the significance of authors to individual venues. Specifically, we develop a co-authorship network considering the author-venue relationship which integrates the statistical relevance of authors to individual venues. The RelRank, an improved PageRank algorithm embedding author relevance, is then proposed to rank authors for each venue. Extensive experiments are carried out to analyse the proposed RelRank in comparison with classic author-level metrics on three datasets of different research domains. We also evaluate the effectiveness of the RelRank and comparison metrics in recommending editorial boards of three venues using test data. Results demonstrate that the RelRank is able to identify not only the high profile scholars but also those who are particularly significant for individual venues. 相似文献
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Co-authorship networks in the digital library research community 总被引：5，自引：0，他引：5
Xiaoming Liu Johan Bollen Michael L. Nelson Herbert Van de Sompel 《Information processing & management》2005,41(6):1462-1480
The field of digital libraries (DLs) coalesced in 1994: the first digital library conferences were held that year, awareness of the World Wide Web was accelerating, and the National Science Foundation awarded $24 Million (US) for the Digital Library Initiative (DLI). In this paper we examine the state of the DL domain after a decade of activity by applying social network analysis to the co-authorship network of the past ACM, IEEE, and joint ACM/IEEE digital library conferences. We base our analysis on a common binary undirectional network model to represent the co-authorship network, and from it we extract several established network measures. We also introduce a weighted directional network model to represent the co-authorship network, for which we define AuthorRank as an indicator of the impact of an individual author in the network. The results are validated against conference program committee members in the same period. The results show clear advantages of PageRank and AuthorRank over degree, closeness and betweenness centrality metrics. We also investigate the amount and nature of international participation in Joint Conference on Digital Libraries (JCDL). 相似文献
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Chunlin Li Jingpan Bai Zhao Wenjun Yang Xihao 《Information processing & management》2019,56(1):91-109
Recently, social network has been paid more and more attention by people. Inaccurate community detection in social network can provide better product designs, accurate information recommendation and public services. Thus, the community detection (CD) algorithm based on network topology and user interests is proposed in this paper. This paper mainly includes two parts. In first part, the focused crawler algorithm is used to acquire the personal tags from the tags posted by other users. Then, the tags are selected from the tag set based on the TFIDF weighting scheme, the semantic extension of tags and the user semantic model. In addition, the tag vector of user interests is derived with the respective tag weight calculated by the improved PageRank algorithm. In second part, for detecting communities, an initial social network, which consists of the direct and unweighted edges and the vertexes with interest vectors, is constructed by considering the following/follower relationship. Furthermore, initial social network is converted into a new social network including the undirected and weighted edges. Then, the weights are calculated by the direction and the interest vectors in the initial social network and the similarity between edges is calculated by the edge weights. The communities are detected by the hierarchical clustering algorithm based on the edge-weighted similarity. Finally, the number of detected communities is detected by the partition density. Also, the extensively experimental study shows that the performance of the proposed user interest detection (PUID) algorithm is better than that of CF algorithm and TFIDF algorithm with respect to F-measure, Precision and Recall. Moreover, Precision of the proposed community detection (PCD) algorithm is improved, on average, up to 8.21% comparing with that of Newman algorithm and up to 41.17% comparing with that of CPM algorithm. 相似文献
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[目的/意义]旨在将科技文献的价值进行量化,提高PageRank算法应用在科技文献排名中的准确性。[方法/过程]在加入时间因子的PageRank算法的改进算法WPageRank的基础上,加入引用相关度进行改进,并计算文献的固有价值,与文献的PageRank值进行加权求和,得到文献的最终价值。[结果/结论]本文提出的方法使新发表的高质量文献也可以获得较高排名,并且使领域内的高质量文献更容易被检索到,同时保证了检索的时效性和主题集中性。 相似文献
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[目的/意义]为弥补现有作者影响力评价指标缺乏内容信息的不足,发现不同研究主题下高影响力的作者,文章给出一种基于主题内容的作者影响力评价方法。[方法/过程]以情报学领域近5年核心期刊的文献为样本,首先利用CTM模型提取样本文献的主题,获得文献作者对不同主题的贡献值;再利用K-means算法对样本文献分类,由此将文献对应的作者划分到特定主题类别下;然后,将作者在某特定主题类别的贡献值与作者发表文献的平均被引频次相结合,设计特定主题类别下作者影响力指标(Author Influence Index in Specific Topic,AII-ST);最后,根据AII-ST值对作者进行影响力排序。[结果/结论]本研究在方法上,通过CTM模型与K-means算法的结合实现了K-means算法初始聚类中心与聚类数目的双重优化;在应用中,作者评价指标AII-ST值能有效限定作者的比较范围,较好地反映作者的研究方向;新指标评价视角新颖、评价结果可靠。 相似文献
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Mining linkage information from the citation graph has been shown to be effective in identifying important literatures. However, the question of how to utilize linkage information from the citation graph to facilitate literature retrieval still remains largely unanswered. In this paper, given the context of biomedical literature retrieval, we first conduct a case study in order to find out whether applying PageRank and HITS algorithms directly to the citation graph is the best way of utilizing citation linkage information for improving biomedical literature retrieval. Second, we propose a probabilistic combination framework for integrating citation information into the content-based information retrieval weighting model. Based on the observations of the case study, we present two strategies for modeling the linkage information contained in the citation graph. The proposed framework provides a theoretical support for the combination of content and linkage information. Under this framework, exhaustive parameter tuning can be avoided. Extensive experiments on three TREC Genomics collections demonstrate the advantages and effectiveness of our proposed methods. 相似文献
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[目的/意义]随着网络和社交媒体的发展,网络"意见领袖"在网络社区的信息传播和交流中发挥着越来越重要的作用,在社会生活的各个方面对网络民意产生巨大的影响。因此,识别网络"意见领袖",掌握其特征和规律成为了网络信息传播研究的重要方面。[方法/过程]在PageRank思想的基础上,利用文本的TF-IDF计算网络社区用户节点的连接强度,以此改进PageRank算法,提出一种LeaderRank方法用来评价网络社区用户节点的重要度,并结合其他指标及BP神经网络进行"意见领袖"的发现实验以及进一步的数据挖掘工作。[结果/结论]实验结果表明,该方法相较于神经网络具有更高的识别率,该方法可以灵活配合其他指标和方法使用,具有更好的适用性、扩展性和稳定性。 相似文献
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《Information processing & management》2022,59(3):102924
The existing credit allocation method of coauthored research paper could not tell the whole story about who did what and the acknowledgment of different parts of the article. When an article is cited, the first author often gets the primary or even full credit, even if the citing paper cites the method part of the article, which is mainly contributed by the second author. This study proposes a context-based author credit (CAC) model to allocate individual credit to coauthors in a multi-authored paper. In the proposed model, coauthor's credit is conceptualized as a directed and weighted connection between citations and contributor roles, where the relationship was decided by citation context. Citation strength was used in the proposed model instead of the number of citing papers which can make the credit of research more precise. The proposed approach can complement existing measures of author credit analysis based on author signature order. In our experiments, the model was validated by fitting to empirical data, a group of highly productive authors’ articles and their citing papers, from PLOS Medicine. The results show that CAC model outperforms prior alternatives such as normal, fractional, harmonic counting and author contribution solely based on contribution list in terms of reflecting the specific performance of coauthors. Besides, the CAC model has a certain sensitivity to the contributions of lower-ranked authors, breaking through the restriction of the author's signature order. This paper also provides the new application of this model in author academic evaluation. 相似文献
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同行评议是当前对科研项目水平进行科学评价的主要方式之一,然而评议过程中专家评审能力的差别将会对科研项目评审结果产生影响。为此,本文提出了一种基于PageRank算法的评审专家信誉度度量方法,该方法首先利用高斯分布函数计算评审专家的评审能力,然后利用PageRank迭代算法对评审专家的信誉度进行求解,最后通过引入时间因子对评审专家的信誉度进行度量。基于同行评议真实数据集上的实验结果验证了本文提出方法的有效性,该方法将为科研项目评审及专家遴选提供有益参考。 相似文献
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In both the UK and Australia there has been a recent move to use citation analysis in the evaluation of the research of individuals. In particular, the future UK Research Excellence Framework (REF), proposes using citation data in the research evaluation of articles published as recently as the year prior to the evaluation. In response to this move, this research develops an indicator at the level of individual articles that, when normalized, can supplement peer review. The new hybrid indicator is the weighted sum of two indicators in common usage: the article’s total number of citations in a citation window, and the Impact Factor of the journal in which the article was published. This research compares this new indicator with the article’s total number of citations in a longer citation window (the standard indicator of article impact). For citation windows of 0 or 1 years, the correlation of the simplified weighted sum with long-term citation is substantially higher than the correlation of the standard indicator of article citation with long-term citation. Moreover, for citation windows of as long as 3 years the standard indicator of citation correlates significantly with the month of publication, in that articles published earlier in the year are on average more highly cited than those published later in the year. By contrast, the skewing of the simplified weighted sum towards articles published early in the year is considerably less than that of the standard indicator. 相似文献
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Fermín L. Cruz Carlos G. VallejoFernando Enrı´quez José A. Troyano 《Information processing & management》2012
In this paper we present the relevance ranking algorithm named PolarityRank. This algorithm is inspired in PageRank, the webpage relevance calculus method used by Google, and generalizes it to deal with graphs having not only positive but also negative weighted arcs. Besides the definition of our algorithm, this paper includes the algebraic justification, the convergence demonstration and an empirical study in which PolarityRank is applied to two unrelated tasks where a graph with positive and negative weights can be built: the calculation of word semantic orientation and instance selection from a learning dataset. 相似文献
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专利引文由于在科技评价过程中具有十分重要的作用,近年来一直是研究的重点。然而,作为专利引文研究理论基石的专利引文关系形成的影响因素问题并没有得到较好的解决。随着网络分析方法的深入,围绕着专利引文网络结构特征的研究出现了大量的研究成果,这些成果都从某种程度上折射出专利引文关系的形成受到了来自属性特征之外关系特征的影响,而现有的以回归方法为基础的统计推断方法难以将这些因素纳入进分析框架中来。本文借鉴指数随机图建模理论框架,将影响专利引用关系形成的若干因素归纳为网络自组织过程,属性特征影响过程与外部情境影响过程等因素,以PATSTAT风能数据为基础,本文根据不同类型的影响过程分别构建了若干独立的过程模型以及综合模型,通过对不同模型参数估计结果以及拟合优度的比较发现：专利的属性特征对于专利引用关系形成的影响被高估了;而引用关系的自组织过程对于专利引用关系的形成产生了更为重要的影响。该研究结论的发现,为下一步改进专利引用关系形成影响因素问题研究指明了方向。 相似文献
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Masaki Eto 《Information processing & management》2019,56(6):102046
This study proposes a novel extended co-citation search technique, which is graph-based document retrieval on a co-citation network containing citation context information. The proposed search expands the scope of the target documents by repetitively spreading the relationship of co-citation in order to obtain relevant documents that are not identified by traditional co-citation searches. Specifically, this search technique is a combination of (a) applying a graph-based algorithm to compute the similarity score on a complicated network, and (b) incorporating co-citation contexts into the process of calculating similarity scores to reduce the negative effects of an increasing number of irrelevant documents. To evaluate the search performance of the proposed search, 10 proposed methods (five representative graph-based algorithms applied to co-citation networks weighted with/without contexts) are compared with two kinds of baselines (a traditional co-citation search with/without contexts) in information retrieval experiments based on two test collections (biomedicine and computer linguistic articles). The experiment results showed that the scores of the normalized discounted cumulative gain ([email protected]) of the proposed methods using co-citation contexts tended to be higher than those of the baselines. In addition, the combination of the random walk with restart (RWR) algorithm and the network weighted with contexts achieved the best search performance among the 10 proposed methods. Thus, it is clarified that the combination of graph-based algorithms and co-citation contexts are effective in improving the performance of co-citation search techniques, and that sole use of a graph-based algorithm is not enough to enhance search performances from the baselines. 相似文献
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学者影响力反映学者的学术水平,甄别综合影响力优异的学者有助于推动科学研究的发展。通过构建基因编辑领域学者的学术文献影响力、学术合作影响力、学术引用影响力、社会影响力以及网络社区影响力间的结构方程模型,探究多类指标对各维度影响力的作用程度,结果发现学术迹、合作引用强度、引文网络PageRank值、Altmetrics-h指数均在各层面影响力有更高的贡献作用,Researchgate类指标在揭示网络社区影响力中作用相当。利用天际线算法对基因编辑领域学者进行综合影响力评价实证,发现涵盖Researchgate指标的评价体系可帮助遴选更多学术水平优秀的学者,结果与领域研究的实际推动者较为一致,进一步证实了该多维综合评价体系的可行性以及Researchgate类指标作为补充性指标进行学术评价的有用性。以上结论均可为科技评价理论拓展和应用实践提供一定参考。 相似文献