Prediction of highly cited papers

In an article written five years ago [arXiv:0809.0522], we described a method for predicting which scientific papers will be highly cited in the future, even if they are currently not highly cited. Applying the method to real citation data we made predictions about papers we believed would end up being well cited. Here we revisit those predictions, five years on, to see how well we did. Among the over 2000 papers in our original data set, we examine the fifty that, by the measures of our previous study, were predicted to do best and we find that they have indeed received substantially more citations in the intervening years than other papers, even after controlling for the number of prior citations. On average these top fifty papers have received 23 times as many citations in the last five years as the average paper in the data set as a whole, and 15 times as many as the average paper in a randomly drawn control group that started out with the same number of citations. Applying our prediction technique to current data, we also make new predictions of papers that we believe will be well cited in the next few years.Comment: 6 pages, 3 figures, 2 table

Why so many published sensitivity analyses are false: a systematic review of sensitivity analysis practices

Sensitivity analysis provides information on the relative importance of model input parameters and assumptions. It is distinct from uncertainty analysis, which addresses the question ‘How uncertain is the prediction?’ Uncertainty analysis needs to map what a model does when selected input assumptions and parameters are left free to vary over their range of existence, and this is equally true of a sensitivity analysis. Despite this, many uncertainty and sensitivity analyses still explore the input space moving along one-dimensional corridors leaving space of the input factors mostly unexplored. Our extensive systematic literature review shows that many highly cited papers (42% in the present analysis) fail the elementary requirement to properly explore the space of the input factors. The results, while discipline-dependent, point to a worrying lack of standards and recognized good practices. We end by exploring possible reasons for this problem, and suggest some guidelines for proper use of the methods

How do we use computational models of cognitive processes?

Previously I outlined a scheme for understanding the usefulness of computational models.1 This scheme was accompanied by two specific proposals. Firstly, that although models have diverse purposes, the purposes of individual modelling efforts should be made explicit. Secondly, that the best use of modelling is in establishing the correspondence between model elements and empirical objects in the form of certain 'explanatory' relationships: prediction, testing, existence proofs and proofs of sufficiency and insufficiency. The current work concerns itself with empirical tests of these two claims. I survey highly cited modelling papers and from an analysis of this corpus conclude that although a diverse range of purposes are represented, neither being accompanied by an explicit statement of purpose nor being a model of my 'explanatory' type are necessary for a modelling paper to become highly cited. Neither are these factors associated with higher rates of citation. The results are situated within a philosophy of science and it is concluded that computational modelling in the cognitive sciences does not consist of a simple Popperian prediction-and-falsification dynamic. Although there may be common principles underlying model construction, they are not captured by this scheme and it is difficult to imagine how they could be captured by any simple formula

Predicting Scientific Success Based on Coauthorship Networks

We address the question to what extent the success of scientific articles is due to social influence. Analyzing a data set of over 100000 publications from the field of Computer Science, we study how centrality in the coauthorship network differs between authors who have highly cited papers and those who do not. We further show that a machine learning classifier, based only on coauthorship network centrality measures at time of publication, is able to predict with high precision whether an article will be highly cited five years after publication. By this we provide quantitative insight into the social dimension of scientific publishing - challenging the perception of citations as an objective, socially unbiased measure of scientific success.Comment: 21 pages, 2 figures, incl. Supplementary Materia

Understanding the Impact of Early Citers on Long-Term Scientific Impact

This paper explores an interesting new dimension to the challenging problem of predicting long-term scientific impact (LTSI) usually measured by the number of citations accumulated by a paper in the long-term. It is well known that early citations (within 1-2 years after publication) acquired by a paper positively affects its LTSI. However, there is no work that investigates if the set of authors who bring in these early citations to a paper also affect its LTSI. In this paper, we demonstrate for the first time, the impact of these authors whom we call early citers (EC) on the LTSI of a paper. Note that this study of the complex dynamics of EC introduces a brand new paradigm in citation behavior analysis. Using a massive computer science bibliographic dataset we identify two distinct categories of EC - we call those authors who have high overall publication/citation count in the dataset as influential and the rest of the authors as non-influential. We investigate three characteristic properties of EC and present an extensive analysis of how each category correlates with LTSI in terms of these properties. In contrast to popular perception, we find that influential EC negatively affects LTSI possibly owing to attention stealing. To motivate this, we present several representative examples from the dataset. A closer inspection of the collaboration network reveals that this stealing effect is more profound if an EC is nearer to the authors of the paper being investigated. As an intuitive use case, we show that incorporating EC properties in the state-of-the-art supervised citation prediction models leads to high performance margins. At the closing, we present an online portal to visualize EC statistics along with the prediction results for a given query paper

Will This Paper Increase Your h-index? Scientific Impact Prediction

Scientific impact plays a central role in the evaluation of the output of scholars, departments, and institutions. A widely used measure of scientific impact is citations, with a growing body of literature focused on predicting the number of citations obtained by any given publication. The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare. Given this limitation, in this work we instead address a related question asked by many academic researchers in the course of writing a paper, namely: "Will this paper increase my h-index?" Using a real academic dataset with over 1.7 million authors, 2 million papers, and 8 million citation relationships from the premier online academic service ArnetMiner, we formalize a novel scientific impact prediction problem to examine several factors that can drive a paper to increase the primary author's h-index. We find that the researcher's authority on the publication topic and the venue in which the paper is published are crucial factors to the increase of the primary author's h-index, while the topic popularity and the co-authors' h-indices are of surprisingly little relevance. By leveraging relevant factors, we find a greater than 87.5% potential predictability for whether a paper will contribute to an author's h-index within five years. As a further experiment, we generate a self-prediction for this paper, estimating that there is a 76% probability that it will contribute to the h-index of the co-author with the highest current h-index in five years. We conclude that our findings on the quantification of scientific impact can help researchers to expand their influence and more effectively leverage their position of "standing on the shoulders of giants."Comment: Proc. of the 8th ACM International Conference on Web Search and Data Mining (WSDM'15

Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora

Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of scholarly authority to find the prominent authors but these authority indices do not differentiate authority based on research topics. We present Latent Topical-Authority Indexing (LTAI) for jointly modeling the topics, citations, and topical authority in a corpus of academic papers. Compared to previous models, LTAI differs in two main aspects. First, it explicitly models the generative process of the citations, rather than treating the citations as given. Second, it models each author's influence on citations of a paper based on the topics of the cited papers, as well as the citing papers. We fit LTAI to four academic corpora: CORA, Arxiv Physics, PNAS, and Citeseer. We compare the performance of LTAI against various baselines, starting with the latent Dirichlet allocation, to the more advanced models including author-link topic model and dynamic author citation topic model. The results show that LTAI achieves improved accuracy over other similar models when predicting words, citations and authors of publications.Comment: Accepted by Transactions of the Association for Computational Linguistics (TACL); to appea