Abstract
Analyzing databases of academic papers citations with the tools of graph and network sciences produced many different results in the past: Publications ranking algorithms, predicting the becoming of their popularity either using the citations only or in association with the co-author or affiliations networks, understanding better the “ethnological aspects” of citation practices. The examination of the dynamical properties of such networks, i.e. how their nodes in-degree grows in time, started more recently. In this paper, we propose a novel ranking algorithm that makes a key use of these growth characteristics (for instance rewarding young, emerging stars more, and old, declining ones less) while requiring much less information and computation. To validate our ranking results and compare them with more established algorithms such as PageRank and FutureRank, four well-known datasets are used.
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Notes
- 1.
The ArXiV HEPTH and HEPPH datasets are available online at the following address http://www.cs.cornell.edu/projects/kddcup/datasets.html. The American Physical Society dataset is available online at the following address http://journals.aps.org/datasets. The PubMed dataset is available online at the following address http://www.ncbi.nlm.nih.gov/pmc/tools/ftp/.
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Waumans, M., Bersini, H. (2017). Ranking Scientific Papers on the Basis of Their Citations Growing Trend. In: Shmueli, E., Barzel, B., Puzis, R. (eds) 3rd International Winter School and Conference on Network Science . NetSci-X 2017. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-55471-6_8
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