ArticleRank: a PageRank-based alternative to numbers of citations for analysing citation networks

Purpose - The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations. Design/methodology/approach - ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets - a citation network based on an early paper on webometrics, and a self-citation network based on the 19 most cited papers in the Journal of Documentation - using citation data taken from the Web of Knowledge database. Findings - ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited and ArticleRank is greatest for the most heavily cited articles in a dataset. Originality/value - This is a novel application of the PageRank algorithm

A principal component analysis of 39 scientific impact measures

The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution

How are new citation-based journal indicators adding to the bibliometric toolbox?

The launching of Scopus and Google Scholar, and methodological developments in Social Network Analysis have made many more indicators for evaluating journals available than the traditional Impact Factor, Cited Half-life, and Immediacy Index of the ISI. In this study, these new indicators are compared with one another and with the older ones. Do the various indicators measure new dimensions of the citation networks, or are they highly correlated among them? Are they robust and relatively stable over time? Two main dimensions are distinguished -- size and impact -- which together shape influence. The H-index combines the two dimensions and can also be considered as an indicator of reach (like Indegree). PageRank is mainly an indicator of size, but has important interactions with centrality measures. The Scimago Journal Ranking (SJR) indicator provides an alternative to the Journal Impact Factor, but the computation is less easy

Investigating the Impact of the Blogsphere: Using PageRank to Determine the Distribution of Attention

Much has been written in recent years about the blogosphere and its impact on political, educational and scientific debates. Lately the issue has received significant attention from the industry. As the blogosphere continues to grow, even doubling its size every six months, this paper investigates its apparent impact on the overall Web itself. We use the popular Google PageRank algorithm which employs a model of Web used to measure the distribution of user attention across sites in the blogosphere. The paper is based on an analysis of the PageRank distribution for 8.8 million blogs in 2005 and 2006. This paper addresses the following key questions: How is PageRank distributed across the blogosphere? Does it indicate the existence of measurable, visible effects of blogs on the overall mediasphere? Can we compare the distribution of attention to blogs as characterised by the PageRank with the situation for other forms of Web content? Has there been a growth in the impact of the blogosphere on the Web over the two years analysed here? Finally, it will also be necessary to examine the limitations of a PageRank-centred approach

What increases (social) media attention: Research impact, author prominence or title attractiveness?

Do only major scientific breakthroughs hit the news and social media, or does a 'catchy' title help to attract public attention? How strong is the connection between the importance of a scientific paper and the (social) media attention it receives? In this study we investigate these questions by analysing the relationship between the observed attention and certain characteristics of scientific papers from two major multidisciplinary journals: Nature Communication (NC) and Proceedings of the National Academy of Sciences (PNAS). We describe papers by features based on the linguistic properties of their titles and centrality measures of their authors in their co-authorship network. We identify linguistic features and collaboration patterns that might be indicators for future attention, and are characteristic to different journals, research disciplines, and media sources.Comment: Paper presented at 23rd International Conference on Science and Technology Indicators (STI 2018) in Leiden, The Netherland

Community Detection and Growth Potential Prediction Using the Stochastic Block Model and the Long Short-term Memory from Patent Citation Networks

Scoring patent documents is very useful for technology management. However, conventional methods are based on static models and, thus, do not reflect the growth potential of the technology cluster of the patent. Because even if the cluster of a patent has no hope of growing, we recognize the patent is important if PageRank or other ranking score is high. Therefore, there arises a necessity of developing citation network clustering and prediction of future citations. In our research, clustering of patent citation networks by Stochastic Block Model was done with the aim of enabling corporate managers and investors to evaluate the scale and life cycle of technology. As a result, we confirmed nested SBM is appropriate for graph clustering of patent citation networks. Also, a high MAPE value was obtained and the direction accuracy achieved a value greater than 50% when predicting growth potential for each cluster by using LSTM.Comment: arXiv admin note: substantial text overlap with arXiv:1904.1204

Personalized PageRank with Node-dependent Restart

Personalized PageRank is an algorithm to classify the improtance of web pages on a user-dependent basis. We introduce two generalizations of Personalized PageRank with node-dependent restart. The first generalization is based on the proportion of visits to nodes before the restart, whereas the second generalization is based on the probability of visited node just before the restart. In the original case of constant restart probability, the two measures coincide. We discuss interesting particular cases of restart probabilities and restart distributions. We show that the both generalizations of Personalized PageRank have an elegant expression connecting the so-called direct and reverse Personalized PageRanks that yield a symmetry property of these Personalized PageRanks

Weighted citation: An indicator of an article's prestige

We propose using the technique of weighted citation to measure an article's prestige. The technique allocates a different weight to each reference by taking into account the impact of citing journals and citation time intervals. Weighted citation captures prestige, whereas citation counts capture popularity. We compare the value variances for popularity and prestige for articles published in the Journal of the American Society for Information Science and Technology from 1998 to 2007, and find that the majority have comparable status.Comment: 17 pages, 6 figure